Data Science Leaders (Domino Data Lab)

Dave Frankenfield , VP Enterprise Data and Analytics at Optum , oversees a team of 2,700 data professionals. How do you structure a team of that size? What functions does it cover? And how does it collaborate with and deliver value to the rest of the company?

In this episode, Dave discusses the strategies he’s used to build his team, the lessons he’s learned, and the advice he has for data science leaders scaling teams of any size in the enterprise.

The conversation covers:
- Building an analytics team from the ground up
- Approaches to managing shadow IT
- Tradeoffs between distributed vs. delegated data science teams

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Legal work may not be an obvious application of data science to many advanced analytics leaders. But that should change.
In this episode, Peter Geovanes, Head of Data Strategy, AI & Analytics at Winston & Strawn, breaks down the nuts and bolts of legal analytics and how it’s revolutionizing the way law firms win new business—and cases. Plus, he shares insight on the types of legal challenges data science can help address inside any organization.
We discuss:
The role of advanced analytics in the legal sphere
Use cases on both the business and practice sides of law
How analytics leaders and general counsels can work together
What’s next in the world of legal analytics  


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The coding, models, and experiments inherent in data science work may have more to do with understanding human well-being than you think.
Machine learning and AI can be applied in ways big and small to further our understanding of human behavior—and influence our well-being.
Takuya Kitagawa, Chief Data Officer & Managing Executive Officer at Rakuten Group, believes there must be a shift toward focusing on well-being when it comes to how brands relate to customers. He joins the show to share his perspective on the future of data science, plus he details his approach to managing a large team spanning many products, cultures, and geographies.
In this episode, we discuss:
The role of ML in unifying the customer experience across multiple products
Managing globally distributed data science teams
Understanding human intention and well-being with technology


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You may not have a formal “portfolio management” function within your data science team, but in all likelihood, you’re executing some of its key components already. 

But being more intentional around portfolio management can pay big dividends. Without it, you could be missing out on a powerful and holistic way of demonstrating the value your team provides to the business.

In this episode, Katya Hall, Director of Enterprise Analytics at McKesson, explains how the portfolio management process sets the groundwork for defining KPIs that track the actual value derived from predictive models and insights. Plus, she shares her thoughts on a process for validating model accuracy and managing risk.

We discuss:
- Tips for working with business counterparts
- Data science portfolio management
- Model risk management
- Supply chain analytics

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Large enterprises will always have some internal groups that are more change-averse than others. But progress often necessitates change, and how well you navigate the change management process can make or break your success as a leader.
Michal Levitzky is the Head of Data & Analytics (CDO) at Migdal Group, a leading insurance and finance company in Israel. Michal has spearheaded the introduction of data and analytics functions at multiple organizations, and she knows a thing or two about negotiating the complexities of change management during analytics transformations.
In this episode, Michal shares her advice for AI leaders driving meaningful change at their own companies. Plus she details her philosophy on structuring data and analytics teams for maximum efficiency and collaboration.
We discuss:
Using experience in fields like accounting as building blocks for leadership in data science
Change management during model-driven transformations
A structure to enable BI and data science functions to better support each other 


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Forward-thinking companies are already embedding machine learning into their business processes—and seeing the payoff of model-driven decisioning. But what about deep learning? How can ambitious data scientists get started with deep learning? How can they satisfy their own curiosity, and eventually apply new approaches to address real business challenges? The field may be more approachable than you think.

In this episode, Eitan Anzenberg, Chief Data Scientist at Bill.com, offers his advice on getting started with deep learning. 

We discuss: 
-Testing, trusting, and understanding your data and your models 
-Advice for reducing bias in highly regulated industries 
-Considerations for getting started with deep learning 
-Challenges of deep learning as a discipline 

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There’s tremendous value in pure data science research. In an enterprise context, however, it all comes down to how learnings and insights from that research can help advance business growth, customer experience, and product innovation.
Sunil Kumar Vuppala is the Director of the Global Artificial Intelligence Accelerator at Ericsson. His career journey from a researcher role to data science leadership has given him years of perspective on how ML professionals and their business side counterparts can build partnerships that pay off in both the near and long term.
In this episode, Sunil shares some of those key lessons on education, communication, and collaboration. Plus, he details a unique MLOps strategy he’s employed to address challenges with scaling model monitoring.
We discuss:
How a research background can inform leadership style
MLOps best practices for scale
Forming mutually beneficial partnerships between business stakeholders and data science teams



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Mike Foley has been building data science teams from scratch since before they were called “data science” teams. His perspective on questions like “Where do I start?” or “How do I get buy-in?” can help leaders growing data science teams of any size avoid some pitfalls along the way.

Currently the Senior Director of Data Science at Hitachi Vantara, Mike joined Dave for a conversation that goes deep into the steps required to stand up a data science practice. Plus, he shared what inspired him to go back to school, and gave listeners a unique peek into the world of marketing analytics.

This episode features Mike’s insight on:

Starting data science practices from scratch
The complexities of marketing analytics
The value of continuous learning


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Many people assume that once you establish a manufacturing line, the hard work is done and things remain relatively static. The reality, especially in electronics manufacturing, is entirely different.
Constantly changing data streams and endlessly dynamic variables present some unique challenges for data scientists in the field. But there are lessons on data sharing, model adoption, and real-time impact that ML professionals in any field can learn from.
In this episode, Alon Malki, Senior Director of Data Science at NI (National Instruments), opens a window into the world of data science in electronics manufacturing. Plus, he shares why human-in-the-loop processes are essential to gaining buy-in for AI in the enterprise.
We discuss:
Data science in electronics manufacturing
Strategies for sharing data to improve manufacturing processes
Human-in-the-loop applications
Looking for challenge-motivated data science talent  


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Data plays a vital role in cancer treatment. In oncology analytics, data analytics can help identify promising treatment strategies, offer better access to affordable care options, and provide critical feedback to medical teams.

In this episode, Susan Hoang , Vice President of Oncology Analytics at McKesson , shares how her team overcomes the inherent challenges of messy healthcare data to deliver insights that can help save lives. Plus, she shares the unique journey that took her from economics and marketing to data science.

We discuss:
- Susan’s unique path to becoming a data science leader
- Sifting through messy healthcare data
- How to define measurable data science outcomes and gain buy-in

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People analytics—the application of data science and analytics in the world of HR—can provide valuable insights into recruitment, retention, and productivity.

But when working with people's sensitive demographic, compensation, and performance data, ethical and privacy considerations must come first.

In this episode, Adam McElhinney, Chief Data Sc ience Officer, VP of Data Insights at Paylocity , explains how his company approaches people analytics, and what all data science leaders can learn from the discipline. Plus, he offers a view into the hiring process Paylocity uses to add top-notch data science talent to its team.

The conversation covers:

People analytics and HR
Data science in the hiring process
Embedding data science into SaaS platforms


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In every industry, people consume data. They work to understand what it can tell them in order to make smarter decisions.
But the nature of data in the world of life sciences presents some unique challenges—and opportunities—for data science.
In this episode, Sidd Bhattacharya, Director of Healthcare Analytics & AI at PwC, dives deep into these dynamics and shares his perspective on how leaders can operationalize AI at life sciences companies.
Plus, we talk about the role data science has played in the fight against COVID-19 and the remarkable effort to develop such highly effective vaccines.
We discuss:
How data science in life sciences compares to other industries
Operationalizing AI and measuring the ROI
Strategic recommendations for data science leaders
AI’s contribution to the fight against COVID-19


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What if we could predict how long our models will last in the field?
Is there a mathematical way to estimate mean time to failure for a specific model?
In this episode, Dave Cole is joined by Celeste Fralick, Chief Data Scientist at McAfee, to discuss AI reliability and how it can help predict model decay.
Celeste also explained:
- What AI reliability measures
- Processes to put in place to measure AI reliability
- The difference between DevOps and MLOps at McAfee
- How adversarial machine learning works
- How to build out a more diverse data science team
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To embed models into SaaS platforms at scale, it pays to have a cross-functional team—software engineers, UX designers, data scientists, machine learning engineers—all working together.
That collaboration allows you to tackle hard challenges around scaling models to work across hundreds of thousands of customers. And it enables you to build something that offers tremendous value across many different use cases.
Jayesh Govindarajan, SVP Data Science & Engineering at Salesforce, joins the show to share how his team makes this a reality. Plus, he talks about the priceless value of customer feedback and the three areas where data science teams should focus their efforts.
We discuss:
- Arriving at data science from a pure engineering background
- Why telemetry is no substitute for customer feedback
- Tips for embedding models into a SaaS product
- The three pillars of work for a data science team
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As a data scientist, you must be able to explain complex ideas in simple ways. Knowing your data, knowing the business, and presenting the data clearly to business stakeholders is an essential part of the role.

Gaia Bellone, SVP - Head of Data Science at KeyBank, has a passion for leading and training her data science team. Her priority: ensuring that her team is successful at communicating data effectively.

In this episode, we discuss:
-Knowing your data and communicating it to the business
-Where to begin when launching a data science program
-International differences in data science

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The best data scientists are continually learning something new, taking on unfamiliar projects, and keeping their skills fresh.
The best leaders in the industry create a culture where teams have opportunities to grow and are able to clearly understand and communicate data science concepts.
In this episode, Dave Cole is joined by Dr. Satyam Priyadarshy, Managing Director for India Center, Technology Fellow, and Chief Data Scientist at Halliburton, to discuss how to explain complex subjects in simple terms for better business outcomes.
Dr. Priyadarshy also explained:
- How his experience as a professor has influenced him as a leader in data science
- What governance he puts in place for his training methodology
- How to cultivate a diverse workforce
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What does it take to turn the latest advances in AI into products that deliver business impact at Walmart levels of global scale?
Srujana Kaddevarmuth is the Senior Director of Data & Machine Learning Programs at Walmart Global Tech. Her team drives data strategy and grapples with data science productization every day. With millions of employees, hundreds of millions of customers, and petabytes of data at any given moment, Walmart offers some unique lessons in the complexities of building teams, processes, and products to effectively leverage AI at scale.
In this episode, Srujana shares a few of those lessons, along with her perspective on nonlinear career paths, organizational collaboration and alignment, and her ongoing fascination with what’s next. Plus, she dives into her passion for fostering diversity in data science and tech, sharing strategies leaders can implement to help bring more women into the field.
We discuss:
What to prioritize when experimenting with next-gen tech
How to use “communities of practice” to align your organization
Solving governance, reproducibility, and knowledge sharing challenges at scale
Bringing more women into data science 


In this season finale episode, host Dave Cole also shares his three biggest takeaways from his many in-depth conversations with leaders in data science.
Stay tuned for a whole new season of Data Science Leaders coming soon! We're just getting started.
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Algorithms can have an outsized impact on society. That’s why many data science leaders have focused a lot of effort recently on defining data literacy and ethics in a way that’s operationalizable in their company culture.
In this episode, Dave Cole speaks with Chris Wiggins, Chief Data Scientist for the New York Times, about why a foundation of data literacy and data ethics is so important for data scientists.
What we talked about:
-Building a data science team at the New York Times
-Creating a data culture
-The relationship between a data science team and a data analyst team
-Necessary soft skills for data scientists
Some resources mentioned during the podcast:
hackNY
Beautiful Data: The Stories Behind Elegant Data Solutions
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Even with the recent rise of specialized data science degree programs, top-notch data science talent can come from anywhere. 
Those in leadership positions have a duty to share their knowledge and support aspiring data scientists, regardless of the unique path that brought them to the field. 
Sidney Madison Prescott, Global Head of Intelligent Automation (RPA, AI, ML) at Spotify, has made a habit of sharing her expertise and giving back. And in the process, she’s built a personal brand that would inspire future leaders in any industry. 
In this episode, Sidney shared her career story, offered advice for building diverse data science teams, and detailed her work in robotic process automation at Spotify. 
We discuss:
Sidney’s career journey and her guidance for women and people of color in data science
How a strong personal brand can open doors to opportunities in tech
Why data science leaders should care about robotic process automation  


Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts. 
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As business leaders become more educated on the value that machine learning can deliver, the demands on data science teams only become greater. Business stakeholders are now interested in much more than the accuracy of predictive models. They’re asking questions about productionization, scalability, and bottom line ROI.
In this episode, Nimit Jain, Head of Data Science at Novartis, joins the show to explain how this sea change is transforming how data scientists approach proof of value. Plus, he talks about how companies are adopting responsible AI practices and provides a window into the world of customer experience analytics.
We discuss:
- How proof of value has evolved over time
- The principles of responsible AI
- Customer experience analytics use cases
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Computer scientists can be fearless, pushing the limits of computational power and the scale of data we can analyze. On the flipside, statisticians can be intensely skeptical, always measuring error and bringing a critical perspective.

According to Chris Volinsky, AVP - Data Science & AI Research at AT&T, it’s these two schools of thought that combine to make data science such a powerful function in business.

In this episode, Chris shares his thoughts on how computer science and statistics fundamentals can help us continue to push data science forward. Plus, he offers advice on how to conduct all-important exploratory data analysis (EDA) effectively.

We discuss:
- How computer scientists influenced data science
- What statistical thought brings to the equation
- Tips and tricks for doing EDA right

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In data science, experimentation is everything. But as a leader, how can you balance experimental work that may never pay off with delivering measurable business value every day?
In this episode, Khatereh Khodavirdi, Global Head of Analytics & Data Science - Global Merchants at PayPal, talks with host Dave Cole about how she has navigated that balance throughout her career, all while building world-class data science teams in the process.
We also discuss:
- Making an impact with data science in the ads business at eBay
- Getting buy-in for data science experiments
- Hiring tips for leaders growing their organizations
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Model governance is vital, especially in heavily regulated industries like insurance.
Strong governance can help ensure that key models are reproducible, explainable, and auditable—all important factors for both internal model development workflows and for external regulatory compliance. But the best governance strategy isn’t always obvious.
Anju Gupta, VP Data Science & Analytics at Northwestern Mutual, is a big believer in establishing model governance practices early, and she shares her thoughts on the topic in the episode. Plus, she talks about some surprising roles on her data science team and the unique value that comes from pairing actuaries with data scientists.
We discuss:
How to establish scalable model governance practices
The intersection of actuarial work and machine learning
Roles you didn’t know you needed on your data science team


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Data science teams are responsible for delivering impactful models, of course.
But they’re also responsible for translating that impact (and all the work that goes into it) for business stakeholders of all kinds.
 
In this episode, Dave Cole is joined by Nate Litton, Vice President, Data & Analytics at Toyota North America, to discuss strategies to strengthen the relationship between data science leaders and their business counterparts.
 
They also talked about:
- How to structure your organization for great partnerships
- Cultivating your teams’ soft skills alongside their technical skills
- How to measure stakeholder engagement and partnership success
- What to do with difficult partnerships
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As more organizations recognize the power of data to transform their decision making (or for it to become a product in its own right), the role of the Chief Data Officer has become critical.
So what are the biggest challenges facing every good CDO? And where do data science teams intersect with that work?
In this episode, Dave Cole is joined by Heidi Lanford, Chief Data Officer at Fitch Group, to discuss strategies for cultivating partnerships between data science leaders and the Chief Data Officer.
They also explored questions such as:

What is the role of a CDO?
What does “data as a product” mean?
How can the CDO enable others to deliver insights?
How do you start a data literacy program?
What’s the deal with “citizen data scientists?”

Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts.
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Who uses the models that we create and how do they use them? Those key questions underpin the notion of responsible AI. 
Since algorithms can have a significant societal impact, it’s vital that data scientists are aware of the broader context in which they may be applied. 
In this episode, Anand Rao, Global Artificial Intelligence Lead at PwC, breaks down why responsible AI should be an important consideration for every data science team. Plus, he explains what you need to be successful in AI consulting, and why a portfolio approach to ROI is the best way to demonstrate value to the business. 
We discuss:
The difference between AI in the 1980s and today
Why data science leaders should care about responsible AI
The ingredients for an effective data science consulting practice
ROI analysis in data science 


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It’s a common refrain among enterprise data science professionals: 70-80% of their time is spent on data wrangling and pipeline building. But what happens if you bring data science and data engineering together under one roof?

Mark Teflian , VP, Data Science and Data Engineering at Charter Communications (Spectrum), joins the show to share how bringing the functions together can help increase efficiency and productivity for everyone at an enterprise scale.

Mark covered:
Why data science and data engineering should be under one roof
How data science helped keep Americans connected when COVID-19 drove massive shifts in internet usage
Different ways to approach embedding data science into production systems

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This is Data Science Leaders from Domino Data Lab, with me, Kjell Carlsson. On this show, you’ll have a front-row seat to conversations featuring world-changing data science leaders. Join us for transformative stories of real people using machine learning to harness the power of data science to tackle the world’s most important challenges.
Get it wherever you get your podcasts!

In a perfect world, healthcare data would always be strategically organized, up-to-date, and easily accessible—all in a patient-centered, privacy-first way. But the reality is much more complex.
Robin Foreman, Director of Data Science at CVS Health, joins the show to discuss the challenging world of data science in clinical trials. She also explains how product analytics can be used on the back end of model implementation to answer the key question of “did it work?”
Robin shared her perspective on:
- Turning a PhD in public health into a career as a data science leader
- Navigating data science and clinical trials
- The life cycle of product analytics
Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts.
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Should your team patent its data science work? With open source such an important part of the data science community, patents almost seem antithetical to the ethos of the field itself.
But it turns out, there are some very good reasons to pursue data science patents in business.
In this episode, Kli Pappas, Associate Director of Global Analytics at Colgate-Palmolive, shares his team's process for deciding whether to patent an algorithmic process—and what benefits it can bring. Plus, he talks about why a statistical background is so important for teams that generate data.
We discuss:
- The transition from getting a PhD in chemistry to the analytics world
- Finding the balance between statistical and computer science backgrounds
- Why you should patent your data science work and how to do it
Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts.
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Data scientists in the healthcare industry face some especially tough challenges. Not only do they have to contend with complex regulatory landscapes impacting the data they can work with, but they’re also constrained by some less than modern processes.

75% of medical communication is still delivered by fax. And that’s just one example.

Derrick Higgins , Head of Enterprise Data Science & AI at Blue Cross and Blue Shield of Illinois, Montana, New Mexico, Oklahoma, and Texas, talks to us about the processes his team has put in place to overcome these unique challenges.

In this episode, we cover:
- The challenges of working in a highly-regulated industry
- Implementing a continuous code review process
- Where IT intersects with the data science life cycle

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When highly disruptive events like the COVID-19 pandemic occur, data science teams may have to throw historical data out the window. Models trained on what happened in the past simply don’t work in a radically different present.
In this episode, Karin Chu, VP Data Science and Digital Analytics at Peapod Digital Labs, discusses how her team is tackling that challenge head on, particularly as the global supply chain crisis impacts sectors from grocery to apparel.
Plus, she explains why two things are so vital to the success of a data science team: ML engineers and a culture of communication.
We discuss:
How data science teams are navigating the supply chain crisis
The vital role of an ML engineer
Tips for communicating about data science in business


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In healthcare, only 14% of scientific discoveries actually make it into clinical practice. But data science, in lockstep with the digital transformation, is helping to change that.
As healthcare data and clinical studies transition to digital form, the opportunity to use data science and AI to generate insights and recommend treatment pathways is greater than ever. And the ability to make healthcare delivery more equitable is within reach.
In this episode, Kaushik Raha, VP Data Science & Health Content Operations at Elsevier, explains how data science is transforming the healthcare industry. Plus, he shares his thoughts on bias and some best practices for operationalizing data science.
We discuss:
How data science is helping to modernize healthcare
Working with clinical analytics to root out bias
Advice for operationalizing data science  


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As compute capability continues to expand, the banking industry is turning more and more to data science to enable better customer experiences.
Use cases have proliferated, from product recommendation engines to predictive customer retention alerts. These innovations can drive real business value, but managing the rollout of process and technology changes always presents interesting challenges.
In this episode, Chun Schiros, SVP, Head of Enterprise Data Science Group at Regions Bank, reveals how her team is leveraging AI solutions to optimize the banking experience. And with insight applicable to data science leaders in any industry, she shares her change management tips for driving adoption of machine learning among data skeptics.
We discuss:
- How data science use cases have evolved in the banking industry
- AI solutions in banking that optimize the customer experience
- Change management tips for winning over data science skeptics
Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts.
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Bias is an ever-present enemy of sound data science in healthcare.
Without proactive measures to mitigate bias in the data used to build and train models, real people can bear the brunt of potentially life-altering negative consequences.
Vikram Bandugula, Senior Director of Data Science at Anthem, knows this issue intimately from his extensive experience in healthcare. He joins the show to share his perspective on bias, plus he details his approach to fostering employee motivation and positive team morale.
In this episode, we discuss:
Problem-solving in data science and healthcare
Managing bias in healthcare data sets and models
Motivating high-performing employees and teams 


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To create sustainable business value, data scientists need to navigate all the elements of what this episode’s guest has dubbed “the data science and analytics value chain.”
So what are those elements? And how can you ensure you hire and develop the team that delivers on each one with every single data science project?
Nancy Hersh, Chief Data Officer at Arcadia, joins the show to break it all down.
We discuss:
Five elements of the data science and analytics value chain
How an apprenticeship model can bring data scientists closer to the business
Unique hiring strategies in an ultra-competitive market


Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts.
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When your data science team is consistently more reactive than proactive in addressing business challenges, it can be difficult to be seen as strategic partners.
But by prioritizing building business domain expertise and always asking about the “why” behind any request, you’ll start to build a rapport and change the nature of the relationship.
In this episode, Indy Mondal, Senior Director of Data Science, AI & Product Insights at DocuSign, explains how to create strong business partnerships to earn data science a critical and strategic seat at the table.
Plus, he shares his unique perspective on the business impact of models and why self-service tools are essential to delivering value.
We discuss:
How to use data science to inform business strategy
Using models to drive efficiency across the organization
The role of self-serve tools in data science 


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Between GDPR, CCPA, and more regulatory frameworks on the horizon, the landscape of personal data—and how it can be used in business—is shifting.
On this episode, John Thompson, Global Head of Advanced Analytics & AI at CSL Behring, joins host Dave Cole to discuss that shift, and a potential future in which we as individuals could be compensated for the use of our data.
Plus, John shares the two types of analytics teams he’s seen work well during his career as a data science leader.
Topics covered include:
- If and how individuals can know what companies are doing with their data
- How GDPR and CCPA portend the future of data
- Structuring, growing, and managing different styles of analytics teams
Check out these resources mentioned during the show:
- Analytics: How to Win With Intelligence
- Building Analytics Teams
Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts.
Can’t see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

We’re in the middle of the fourth industrial revolution. Industry 4.0 encompasses the use of advanced automation and analytics in manufacturing. So how is data science driving value in Industry 4.0?
In this episode, Dave Cole is joined by Paul Turner, Vice President Industry 4.0 Applications & Analytics at Stanley Black & Decker, to break down everything you need to know.
We discuss:
-The definition and foundational pillars of Industry 4.0
-Three approaches to Industry 4.0
-Balancing data science and domain expertise to deliver value
-Inspiring action through predictive analytics
Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts.
Can't see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

Data science jobs outnumber data scientists by three to one. The industry is looking for ways to close that gap, including turning to the concept of the citizen data scientist.

But in today’s episode, Romain Ramora , Head of Data Science & Innovation - Supply Chain at Cisco , shares why he thinks we shouldn’t be putting critical models in the hands of people lacking the proper expertise.

Romain shared his perspective on:
- How a background in risk analytics and consulting prepared him for a move into supply chain analytics
- The effectiveness of the citizen data scientist
- Who should lead a data science project

Tune in on Apple Podcasts , Spotify , our website , or wherever you listen to podcasts.

Can’t see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

The field of bioinformatics plays a critical role in medical breakthroughs like the COVID-19 vaccine.

Fiona Hyland , Director of R&D, DNA Sequencing Informatics at Thermo Fisher Scientific , teaches all of us about how it happened in the latest episode of Data Science Leaders.

What we talked about:
- A quick run through genetics and bioinformatics terminology
- Bioinformatics, and the genetics of cancer and the coronavirus
- The role of data science in the development of the COVID-19 vaccine

Check out these resources we mentioned during the podcast:
- COSMIC genetic database
- The 1000 Genomes Project
- gnomAD Genome Aggregation Database
Tune in on Apple Podcasts , Spotify , our website , or wherever you listen to podcasts.

Listening on a desktop & can’t see the links? Just search for Data Science Leaders in your favorite podcast player.

Talent is pouring into data science, even though it always seems like there’s not enough to meet demand. Learning opportunities for people getting into the field have exploded in just the past decade. 

That means standing out from the crowd—both as a leader and as a practitioner—has become more important than ever before.

In this episode, Bob Bress, Head of Data Science at FreeWheel, explains how professionals at all levels can position themselves to win in a burgeoning market. Plus, he offers advice on how data science leaders can stimulate collaboration and intellectual curiosity within their organizations.

We discuss:
- How to stand out from your peers
- Intellectual curiosity, innovation, and collaboration in large organizations
- Being the CEO of the data science project you’re working on

Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts.

Can’t see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

Without a clearly defined methodology, complex projects with multiple technical and business stakeholders often fall apart. The risk is especially high when trying to scale data science work in an enterprise organization. 
That’s why David Von Dollen, Head of AI at Volkswagen of America, integrated agile methodology with CRISP-DM to help his team navigate roadblocks and accelerate progress on the path to model deployment. He shares how this hybrid approach enables his team to be more strategic about project lifecycles, unlocking real business impact even faster. 
Plus, David provides advice for building relationships with key business stakeholders and shares his philosophy on using the art of data science to benefit humanity. 
We discuss:
Implementing hybrid CRISP-DM and agile methodologies
Building relationships with stakeholders across the business
Using data science to solve challenges outside of work      


Mentioned during the show:
DataKind     


Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts. 
Can’t see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

There are many ways to structure a data science function in a global enterprise. But what’s been the winning strategy for global technology distributor Ingram Micro? Creating a data science “nerve center.”
Centralizing data science talent has helped elevate analytics at Ingram Micro to better solve complex business problems using machine learning and AI.
In this episode, Tim Suhling, VP Global Business Intelligence at Ingram Micro, explains how it all happened, and what data science leaders everywhere can learn from the transformation. Plus, he shares his perspective on how data science can impact “Customer 360” programs and different approaches to measuring the success of models.
We discuss:
- The relationship between data science and business intelligence
- Embarking on a customer 360 initiative
- Measuring the effectiveness of data science
Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts.
Can’t see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

Data science operationalization is a simple enough concept.
But in practice it can be a complicated and often overwhelming challenge.
In this episode, Dave Cole is joined by Nishan Subedi, VP, Algorithms at Overstock.com, to discuss the best way to operationalize data science.
Nishan talked about:
- The data science experts that make up the team at Overstock
- Strategies to improve search and measure search success
- The difference between ML engineers and ML scientists
- How to design teams around product orientation
Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts.
Listening on a desktop & can’t see the links? Just search for Data Science Leaders in your favorite podcast player.

The title of “Data Scientist” leapt into prominence in 2012 when the Harvard Business Review named it the “sexiest job of the 21st century.” Almost ten years later, what’s changed? And what’s next?

In this episode, Dave Cole is joined by Mike Tamir , Chief ML Scientist and Head of Machine Learning/AI at SIG , to break down the shifting trends in data science, NLP, and ML—and what it all means for leaders in the field.

The conversation covers:
- The past, present, and future of data science
- The different roles and responsibilities within a data science team
- New and exciting advancements in NLP
- When models are right for the wrong reasons

For daily news and insights on all things data science, follow @MikeTamir on Twitter.

Tune in on Apple Podcasts , Spotify , our website , or wherever you listen to podcasts.

Can’t see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

Is your team mining all available data to inform your business strategy and grow revenue? Is your company prepared to compete against others who are?
If you’re like most, the answer is probably no.
How can you future-proof your organization and take steps toward an autonomous enterprise?
Janet George is an enterprise AI leader and author with experience across companies including Oracle, Apple, Accenture, Yahoo!, eBay, and more. She joins the show to discuss the meaning of autonomous enterprise and the process required for true transformation.
We discuss:
What is an autonomous enterprise?
Where are companies falling short in their data transformation?
The investment and first steps required on the transformation journey
How to prioritize data projects for a larger impact on revenue


Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts.
Can’t see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

Managing a large enterprise team of data scientists can be a complicated undertaking. There are so many opportunities, big and small, to serve the business with AI and machine learning. How do you ensure your teams are focused on the big picture without getting bogged down in the minutiae of the day to day?
Jan Neumann, Executive Director, Machine Learning at Comcast, leads a team of about 300 data scientists, divided into eight different focus areas. If anyone knows how to manage a large data science team, it’s him.
In this episode, he shares his strategies for effectively managing a team of this scale in the enterprise. Plus, he explains why he prioritizes continued learning, and shares tips for building out a feature store.
We discuss:
- Managing large data science teams at scale
- Making time to gain knowledge from the ML community
- What a feature store is and why data scientists should care
Mentioned during the podcast:
- The Idealcast with Gene Kim
- Mik + One with Mik Kersten
- a16z Podcast
- Yannic Kilcher on YouTube
Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts.
Can’t see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

Who’s the best person to share the secrets of Data Science leaders? Try someone who has spent the last year interviewing them! Former industry analyst and new host of the podcast, Dr. Kjell Carlsson, interviews Dave Cole on all surprising things he’s learned in hosting the nearly 50 episodes of season 1.

The two delve into various topics, such as how you may unexpectedly become a data science leader, the experimental foundation of data science, the importance of data science leaders being a part of strategic conversations, and the hard-won best practices that have emerged. They also touch on the sales and marketing aspects of being a data science leader, and the evolving future of data science, and the data science leader.

Join as we discuss:

• The unexpected origins of data science leaders
• The emerging consensus on how to drive impact with data science
• How and why data science is moving higher and higher in organizations
• The future outlook for data science, and its leaders

When it comes to driving business impact with AI, there are no silver bullets, but data science product management comes pretty close. It could well be the key to bridging the gap between business and technical teams, designing solutions to meet the business need, spurring ideas from experimentation to implementation, and driving continuous improvement. But how do you build a product management capability for data science?

In this episode, Alek Liskov, Director of AI & Data Product Management at Verizon, shares their hard-won best practices in building data science product teams and their phenomenal successes in delivering AI-driven impact.
Join us as we discuss:

• The emerging discipline of data science product management
• How to build durable product teams for data science
• Where to find and how to develop data science product managers

What’s different about the AI wave today versus the 1980s and what do the latest advances reveal about our human intelligence? We’re behind the scenes at Rev4 with Steven Levy, best selling author and Editor at Large at WIRED. Steven shares insights he’s built over the past four decades writing about AI and the people (like Marvin Minskey) and companies (like Google and Facebook) that have brought us to where we are today.

Join us as we discuss:

• A brief history of AI - from dashed hopes to triumphant transformers
• How AI is helping us understand human intelligence
• The regulatory risks of anthropomorphizing AI

“It is on the shoulders of leaders that they build and maintain an ethical AI risk program.” That’s the message Reid Blackman – author of “Ethical Machines” and founder CEO at Virtue Consultants – shares in this episode. He discusses the real ethical AI concerns — blackbox models, bias, hallucinations, privacy violations and more — and explains the crucial need for leadership accountability, buy-in from the very top of the organization, and a multi-party effort in building and maintaining AI ethical risk programs.

Join us as we discuss:

• Why AI poses greater ethical risks than other technologies (and humans)
• Leadership and the other key elements of a successful AI / digital ethics program
• The importance of explainability

Generative AI is here and, unless you’ve been cloistered in a cave, you already know it’s making waves in nearly every industry. But when it comes to this shiny new technology, separating fact from fiction can become quite a challenge.

Luckily, in this episode, Rowan Curran, Analyst at Forrester, joins the show to demystify the latest leaps in AI tech, help you apply it to your business today, and give a glimpse of how it will affect the business landscape of tomorrow.

Join us as we discuss:

• Separate generative AI facts and fiction
• Take a closer look at AI applications you can start using today
• Examine the future of AI and its impacts on the workforce and the workplace

It’s been said: “When everything is important, nothing is important.” So how do you succeed with AI-driven transformation where everything – across people, process, and technology – is important? It requires leadership, a deliberate strategy, and ongoing organizational change.

Here to share insight on these transformational challenges and best-practices are Jen Stave and Catherine Feldman from the Digital, Data, and Design (D^3) Institute at Harvard. In this wide-ranging conversation, the duo draws upon seminal research from the Harvard Business School – such as professor Clay Christensen’s theory of Disruption – to explain how organizations must adapt their business and operating models, and make experimentation part of their organizational DNA.

Join us as we discuss:

• Disruption and the reasons so many AI projects fail
• The need for a holistic approach and strong leadership for AI success
• Applying a jobs to be done” approach to generative AI

Also don’t miss HBS professor Karim Lakhani’s Rev 4 Keynote, “Competing in the Age of AI”.

Who doesn’t have a data science talent gap? Anyone? Most organizations struggle to realize their AI ambitions because of a lack of data science skills, a disconnect between the technology and the business domain, and a lack of leadership experience with AI.

Halliburton has been solving all three of these challenges with one of the earliest and largest corporate data science programs in the energy sector.

In today’s episode of Data Science Leaders, we are extremely fortunate to be joined by Dr. Satyam Priyadarshy, Managing Director, Technology Fellow and Chief Data Scientist at Halliburton who shares their best practices for upskilling talent, bridging the data science - business divide, and ensuring executive engagement.

Join us as we discuss:

• How to upskill existing domain experts on data science methods
• How to engage and drive alignment with corporate stakeholders through workshops
• The benefits of upskilling domain experts on code-based data science tools
• The importance of involving and upskilling leadership

How do you trust black-box AI models with decisions that will make-or-break your business?

This week we speak with Gregory Zuckerman -- special writer at the Wall Street Journal and author of The New York Times bestseller of The Man Who Solved the Market -- to find out how the pioneers in algorithmic trading learned to stop worrying and trust their AI systems.

Join us as we discuss:

• How trust in AI relies on trust in people and processes
• The limits of explainability and transparency
• The power of systems over stories

AI has enormous potential for good, not least in helping us make more ethical, sustainable decisions as investors and consumers. In this week’s episode Ron Potok, Head of Data Science at Clarity AI, explains how AI helps us overcome the challenges of collecting, normalizing and assessing Environmental, Social and Governance (ESG) data and making that data useful and convenient to humans when making decisions. Indeed, he reveals how AI can bring transparency to human-only ESG ratings that can be more opaque and prone to bias than an AI model, and the benefits of leveraging humans and AI models in tandem.

Join us as we discuss:

• Overcoming the ESG data quality challenges with AI
• Leveraging AI to contextualize data and drive consistency
• How AI can provide greater transparency than human-only ratings

Every organization has an abundance of outlier detection use cases, but how do you turn them into repeatable, scalable AI products that drive a virtuous cycle of adoption and impact?

To answer this question, Jan Zirnstein, Senior Data Science Director at Honeywell,. shares their best practices for successfully driving value using anomaly detection, how to build trust with stakeholders, and the importance of both product management and software development resources.

Join us as we discuss:

• How to spark a virtuous cycle with anomaly detection use cases
• Driving continuous improvement by transitioning from unsupervised to supervised machine learning
• Aligning the model development and software development lifecycles

Most experts agree that AI isn’t about replacing human intelligence, but about improving it. When it comes to education, we should take this literally.

In this episode we discuss how to use AI to transform how we learn with Stephen Kosslyn, President of Active Learning Sciences and Founder and Chief Academic Officer of Foundry College. Stephen brings unparalleled expertise when it comes to using AI in education from his remarkable career spanning leadership roles at Harvard, Stanford, and Minerva University, but also thanks to his recent book “Active Learning with AI: A Practical Guide”.

Join us as we discuss:

• How Generative AI can make learning more effective and scalable
• How to design educational programs, create training experiences, and assess student understanding using Generative AI
• Overcoming the challenges of embracing AI in the education sector

For more on the science of active learning and detailed, practical Generative AI examples, please check out Stephen’s new book, available now.

How do you implement an enterprise-grade GenAI application that serves millions of users a day? By focusing your application and building the capabilities for operationalizing it at scale.

Join our upcoming fireside chat with Domino's SVP of Product, Chris Lauren, who will share lessons learned while operationalizing the world’s first enterprise-grade GenAI application to be used on a global scale, Github Copilot.

Join us to learn:

• Success factors for GenAI use cases
• Common challenges and how to avoid them
• Key capabilities for operationalizing GenAI models at scale
• Inferencing GenAI models cost-effectively

How do you achieve success as a Chief Data Officer? It is a role that is more important, yet more challenging, than it has ever been, with a rapidly expanding set of expectations from stakeholders in every part of the business.

Here to help us understand the CDO role, its evolution, and the keys to success is Gary Barr, Global Chief Data Officer at Legal & General Investment Management (LGIM). Drawing from his wealth of experience, Gary speaks about the three incarnations of the CDO – from data governance champion to air traffic-controller of AI-driven transformation – as well as the dangers of dividing teams into “offense” and "defense”, the goal of the data mesh, and why AI regulation should be welcomed, not feared.

Join us as we discuss:

• The rapid evolution of the CDO mandate and its responsibilities
• Changing the operating model for data and AI adoption
• The importance of qualitative and sentimental measures of ROI

ChatGPT wasn’t the beginning of generative AI, but it did spark the GenAI revolution. Now, one year since it was launched, how much progress have we made, what impact is GenAI delivering, what are the real risks, and what developments are just around the corner? Join this session with the titan of the data science community, Anaconda CEO Peter Wang, and Dr. Kjell Carlsson, Head of AI Strategy at Domino Data Lab, where we will cover:

• The state of GenAI: where GenAI is delivering and missing expectations
• The challenges: the real risks and remaining barriers to impact
• The future: what advances are underway and what can we expect over the next year

2023 has been an exciting year for AI, but it’s nothing in comparison to what we will see in 2024! Expect to see sensational successes amid the debris of projects that were set up for failure, a flowering of predictive AI, and the emergence of the scariest thing in AI to date (EU regulation). Tune in to this episode where Dr. Kjell Carlsson shares his top predictions for AI in 2024 and get ready for a year of scandal, fraud, plummeting processor prices, and ascendant AI leaders. Also, goodbye quantum computing!

Happy holidays from all of us at Domino Data Lab and the Data Science Leaders podcast.

Imagine Generative AI handling tens of thousands of conversations with your customers daily. Science fiction? Not at Bolt where this has been in production since the summer of 2023. In this episode, Mikhail Korolev – head of the data science team at Bolt’s food delivery service – shares the challenges and hard earned best practices for operationalizing a GenAI application that dramatically lowers cost while also increasing customer satisfaction.

Join us as we discuss:

• How to leverage GenAI to automate customer service conversations
• How to manage inconsistency and mitigate risk in GenAI apps
• How to protect sensitive data and comply with regulatory requirements with GenAI

The biggest challenges to driving impact with AI have little to do with AI and everything to do with humans. Nowhere is this greater than with GenAI where myths and misconceptions abound as to how organizations should be designing, developing and operationalizing GenAI-based applications. In this episode with Rowan Curran, industry analyst at Forrester Research, we debunk the most harmful myths and discuss how AI teams are shattering these myths and delivering transformative outcomes.

Join us as we discuss:

• The role of data scientists and ML engineers in GenAI projects
• Successful approaches to prompt engineering
• The linkages between MLOps and LLMOps

What’s the biggest problem in AI today? It’s that far too few projects make it to deployment. In this episode, Eric Siegel, founder of the long-running Machine Learning Week conference and creator of the first (and perhaps only) ML music video, tells us about his new book, The AI Playbook and the bizML framework for aligning stakeholders and maximizing the chance for deployment and impact.

Join us as we discuss:

• Causes behind the high rates of AI project failure
• Critical project steps for ensuring deployment
• Humor as a means to bridge the gap in AI understanding

And check out:

• The AI Playbook: Mastering the Rare Art of Machine Learning Deployment
• The entire Predict This music video. You won’t regret it.
• Machine Learning Week June 4-7, 2024

There is no such thing as an AI drug, but AI and ML-models are driving the next wave of new treatments. In this episode, Brandon Allgood, Chief Data Officer at FogPharma and serial entrepreneur at the intersection of ML and Biopharma, shares his insights on how AI is disrupting the traditional process of drug discovery and development.

Join us as we discuss:

• Why AI is so powerful for drug discovery
• What data science needs to learn from engineering
• How drug discovery processes need to be rebuilt with AI models at their core

What’s just as important as the government keeping us safe from AI? Government leveraging AI to keep us safe!

In this episode, we interview Joel Meyer – former head of strategy at the Department of Homeland Security (DHS) and the person who drove the creation of the DHS AI Task Force. Joel shares how they identified key areas where they could apply AI to improve national safety and security, such as combating fentanyl and child sexual exploitation and abuse, and the steps that the federal government is taking to build AI capabilities across the public sector.

Join us as we discuss:

• Key areas where US government agencies are looking to leverage AI to improve mission effectiveness
• The people, process, and technology steps that government agencies are implementing to scale AI and how they apply to the private sector
• The importance and value of Responsible AI in public sector use cases and beyond

Wouldn’t it be great if there was a commonly agreed-upon framework for executing all AI projects successfully? Well, there isn’t one. However, there is CRISP-DM, the antediluvian “Cross-Industry Standard Process for Data Mining”, but you need to expand, modernize and adapt this framework for success at your organization.

In this episode from the archive, Dave Cole interviews David Von Dollen, former Head of AI at Volkswagen of America, about how they integrated CRISP-DM into an Agile process to drive more rapid iteration and, ultimately, more successful AI projects.

Trip planning may well be the perfect AI use case. Too much information, too many combinations, and too little time —for humans, but not for Tripadvisor’s AI Trips. In this episode Rahul Todkar, VP Head of Data and AI, shares the secrets to building a trusted GenAI solution at internet scale and discusses the similarities and differences between data leadership roles at digitally native companies and more traditional enterprises.

Join us as we discuss:

• How to use GenAI to unlock first party data
• The ideal GenAI development team
• The evolving role of data and AI leaders

A human being consists of billions of cells, each with the same genetic code but interacting in a myriad ways that can eventually translate into disease. Understanding and treating that disease is, in essence, a data problem. But how do you unlock that data and how do you change an organization to systematically use that data to improve decision-making and accelerate drug discovery? 

In this episode, we speak with Volodimir Olexiouk, Director of Scientific Engagement and Data Science Team Lead at BioLizard, about best practices for overcoming the data challenges for AI-driven drug discovery and combining scientific expertise with data science for augmented intelligence in the life sciences. 

Join us as we discuss:

• The challenges in discerning correlation from causation and integrating domain expertise
• How bridging expertise gaps and merging data silos in pharmaceutical companies radically improves drug-discovery processes 
• The promise AI holds for swifter and more effective responses to future pandemics

GenAI is evolving at a breakneck pace, matched only by the startups that are looking to commercialize it. So what better way to understand the latest GenAI trends than to ask a venture capitalist specializing in AI? 

In this episode, we speak with James Cham, partner at Bloomberg Beta, about the state of GenAI – where it is delivering value today – and the challenges preventing firms from moving from incremental GenAI-driven productivity gains, to truly disruptive GenAI use cases. Along the way, we cover the problem of treating GenAI like software development, the rapidly changing economics of GenAI, and the key to success with all types of AI which is – as always – understanding people. 

Join us as we discuss:

• The cultural challenges preventing companies from unlocking the disruptive potential of GenAI
• Why developers don’t get data-powered applications (and why data scientists need product thinking) 
• How GenAI can change our engagement with technology (by killing the GUI)

AI leaders. Why do we need them? How do you become one? And above all, how do you keep your job as one?

In this episode, we are joined by guest speaker Mike Gualtieri, VP and Principal Analyst at Forrester, and we unpack the opportunities, pitfalls, and best practices of the AI leader role. He shares the pivotal role of AI leaders in catalyzing organizational transformation, their unique skill set that must encompass data science, business acumen, and software engineering, their importance in navigating the evolving regulatory landscape surrounding AI, and the need for platforms to facilitate rigorous auditing and compliance measures to foster trust and transparency.

Join us as we discuss:

• The strategic imperative for AI leaders to curate a diversified portfolio of AI initiatives 
• The multifaceted nature of AI risk management, spanning legal, ethical, and societal dimensions
• The formidable challenges inherent in navigating and enforcing AI-centric regulations amidst rapid technological advancement

How do you craft and implement a strategy to transform an organization with AI? Not just to build a growing portfolio of successful AI projects, but to fundamentally re-engineer the organization’s core processes, to radically increase productivity, to overhaul the company’s tech stack, and to prepare it for a future of AI-driven competition.

In this episode, Akshaya Murthy, who leads the AI efforts for Operations at Zendesk joins us to discuss the mandate and toolkit of the AI transformation leader, the importance of strategy for AI impact, the AI transformation efforts at Zendesk, and their successes to date. 

Join us as we discuss:

• The AI transformation leader: mandate and skillset 
• Strategy: the misunderstood and frequently forgotten key to AI impact
• GenAI: the transformation leader’s new best tool for rapid impact 
• Process transformation: the goal of AI transformation

How do you enable AI, data science and analytics on petabyte-scale data, with extremely stringent privacy and security requirements?

This episode comes to you from the RevX-New York conference where we had a fireside chat with Ivan Black - Director in charge of ML, AI, and analytics platforms at the US financial services regulator FINRA. 

Join us as we discuss:

• The challenges of enabling AI on massive, rapidly growing financial datasets
• Talent strategies to support the rapidly changing AI ecosystem
• The importance of AI governance and reproducibility
• Managing cloud costs

To see all of the sessions at the RevX conferences or to find information about attending upcoming ones go to domino.ai/revx.

AI is not all about the data, however, your ability to develop and deploy efficient data pipelines is absolutely critical for unlocking the power of AI at scale. But how do you manage modern data pipelines for AI and how do you deal with fragmented ecosystems and spiraling costs? 

In this episode, brought to you from the RevX Philadelphia conference,  Richard Swakla, AI/ML Specialist at NetApp, joins us to discuss the current trends and best practices in the life sciences around data and AI. 

Join us as we discuss:

• The role of AI in enhancing productivity in healthcare and the life sciences, particularly in drug discovery,  claims processing and fraud detection.
• The growing importance of hybrid cloud solutions to balance cost, efficiency, and infrastructure access.
• Challenges in transitioning AI projects from pilots to production due to high costs and rapidly evolving models.
  
  

To see all of the sessions at the RevX conferences go to domino.ai/revx. 

How do you deliver value with responsible AI, who is responsible for it, how do you put it into practice, and could we use AI to make our organizations more ethical?  

This episode comes to you from the RevX conference in London, where we asked these questions of Chris Wiggins, Chief Data Scientist at the New York Times. He is also Professor of Applied Mathematics at Columbia University and author of the books “How Data Happened: A History from the Age of Reason to the Age of Algorithms” and “Data Science in Context”.

Join us as we discuss:

• What we can learn from the history of research ethics and data legislation
• The need for clear principles and defined ownership to ensure ethical AI
• The translation of ethical principles into checklists, standards, and product decisions
• The importance of benchmarking AI against human performance and addressing how human biases in data lead to biased AI outcomes

To see all of the sessions at the RevX conferences go to domino.ai/revx. 

How do you deliver impact with AI and ML and cut development time by weeks and even months? By understanding your customer, building trust, and managing risk. 

Done well, effective and responsible AI practices can be the secret to faster implementation, adoption, and performance at lower cost and risk.

In this episode with Dr. Alex Manasson, Data Science Leader for the Americas at International Flavors and Fragrances (IFF), we uncover their best practices for managing risk and driving rapid AI development and adoption in the safety-focused world of manufacturing.. Dr. Manassof shares insights on balancing statistical process control with predictive modeling, the importance of adapting your data collection processes, and the pros and cons of digital twins. Discover practical tips and strategies for implementing AI and ML tools to boost efficiency and foster trust in high-stakes environments. 

AI is disrupting marketing, but the biggest threat isn’t AI systems misbehaving, it is the unintended consequences of AI systems performing exactly what they were intended to do.

In this interview with Dr. Daniel Hulme, Chief AI Officer at WPP and CEO of Satalia, we discuss the ways that AI is transforming marketing – from accelerating content creation and maximizing activation to exploring the creative landscape and creating “brains” that ensure it is responsible and legal. Also, tune in for fascinating discussions of AI consciousness and what it means to be a Chief AI Officer.    

Join us as we discuss:

• The greatest GenAI opportunities in marketing and beyond
• How to maximize AI impact with decision optimization
• Responsible AI and the challenges of AI systems going very right
• The emerging field of AI consciousness
• The Chief AI Officer: why you need one and the prerequisites for success  

For more information about the new research organization focused on AI consciousness co-founded by Daniel Hulme see conscium.com and his interview on the London Futurists Podcast.

How do you drive digital strategy and transformation with AI? Do you need an AI strategy or a business strategy that intelligently leverages AI?

In this episode, we delve into the challenge of driving transformation with AI in insurance with Fu'ad Butt, VP Head of Digital Strategy and Automation at OneAmerica. Fu’ad shares his best practices for identifying and executing AI projects. These range from how to identify the most promising use cases (hint: focus on augmented intelligence, but tie it to business value) to executing them successfully (build a test and learn process, and use multifunctional pods).  

Join us as we discuss:

• The role of AI in digital strategy today.
• Overcoming the challenge of aligning AI to business value.
• Experimenting efficiently with digital twins and a contrarian in the loop.
• Breaking hierarchies with multifunctional pods for faster impact.

What capabilities do you need to take advantage of AI and what changes will you need to make to your IT architecture? Well, let’s look at the data.

In this episode, Shawn Rogers, CEO and Fellow at BARC US, shares the results of their survey on how enterprises are optimizing their architecture for AI innovation. Shawn unpacks data on everything from the biggest obstacles to delivering AI impact to how companies are sourcing their AI capabilities. 

Join us as we discuss:

• The AI talent gap and the strategies that firms are using to close it
• The myriad AI capabilities that high-readiness firms are implementing
• The need to manage AI costs upfront to ensure deployment
• The perpetual question of build vs. buy (or both!)
• The crucial need for AI governance to ensure approval and adoption

Download the full report here.

Data privacy - why is it so important in the age of AI, why is it so difficult, and what should we be doing to improve it in our organizations?

In this episode we speak with Dr. Rebecca Balebako, privacy engineer and Chief Privacy Officer at Lotic.ai, about why AI makes privacy more important than ever, the common misconceptions around data protection, who should own privacy, and the benefits and limitations of privacy enhancing technologies (PETs).

Join us as we discuss:

• The value of privacy (~5% of annual profit)
• The many definitions of privacy.
• The top 3 misconceptions: no one cares, it can be fixed with a privacy policy, and privacy is a blocker.
• Who should own privacy in your organization.
• PETs vs. data hygiene

For more on Rebecca’s consulting on privacy strategy, coaching and how to build a privacy advocate team see https://www.privacyengineer.ch/ and for more on privacy methods check out the book “Practical Data Privacy” by Katharine Jarmul.

AI governance is more important than ever, but confusion reigns about basic questions such as: what it is, why it is important and what we should do about it. As an AI, data science, or business leader, you need to dispel these governance misconceptions in order to manage AI risk and ensure the safety and reliability necessary to drive adoption and impact. The ability of your organization to drive impact with AI, and potentially your career, depends on it.

Join us as host Kjell Carlsson, gives an opinionated take on the basic questions of AI governance:

• Why you should care about AI governance (and why it goes beyond ethics and regulation)
• What AI governance is (and how it differs from ethical AI, responsible AI, and trustworthy AI)
• Why AI governance is difficult (and the gap between frameworks and practices)
• What AI governance looks like in practice (across the AI/ML lifecycle)
• What we need for AI governance (and the capabilities to do it at scale) 

  

AI governance is no longer a hypothetical consideration—it's critical not only to meet regulatory requirements but also to build trust, drive adoption, and deliver tangible impact with AI. In this episode, we bring together experts Jared Vaudrey and Dr. Dylan Bobby Storey, who have spent years developing AI solutions in heavily regulated industries. They share their hard-won insights on the realities of implementing AI governance across a wide range of organizations and use cases.

Join us as we discuss:

• The value of AI governance for driving adoption and innovation
• How governance principles apply to traditional ML and genAI
• The common challenges of governance 
• Real-world examples and best practices from advanced teams integrating AI governance
• How automation helps scale governance and provide the flexibility to support future regulation

The EU AI Act, is it the first step towards comprehensive AI regulation that will make us all safer, or is it the scariest thing in AI today, or both?

In this episode we speak with Adam Gale, Field CTO for AI and Regulatory Compliance at NetApp, to demystify the EU AI act and discuss future-proof strategies to ensure compliance.Join us as we discuss:

• The core requirements of the act and how they impact your use cases
• Gray areas to look out for in the act
• Compliance with existing regulations such as DORA and GDPR
• Core governance capabilities that enable compliance for the EU AI act and future regulation

Think you have a hard time driving AI adoption in your organization? Try driving AI transformation in the largest, most regulated organization on the planet – the Pentagon. 

In this episode, we sit down with Dr. Chris Kirchhoff – co-author of Unit X: How the Pentagon and Silicon Valley are Transforming the Future of War and former Director of Strategic Planning for the National Security Council – to uncover how the Department of Defense is accelerating the adoption of AI. Dr. Kirchhoff shares invaluable lessons on managing disruption and fostering innovation at scale.
Join us as we discuss:

• How leadership and experimentation drove the success of the Defense Innovation Unit
• The importance of developing new frameworks for procurement for AI adoption
• Insights from modern warfare: drone technology and the shift to consumer-driven innovation
• Best practices for scaling technological transformation across any large organization

How can organizations drive transformative AI innovation while effectively managing its inherent risks? Is governance a bottleneck or can it become a catalyst for AI success?
In this webinar, we explore the critical role of AI governance with Brandon Purcell, VP Principal Analyst at Forrester Research, and Theo Linnemann, Data Scientist at the Federal Reserve Bank of New York. Together, they share actionable insights into the principles, practices, and policies necessary for ensuring the safe use of AI that drives real-world impact.

Join us as we discuss:

• The evolution of AI governance and why it has become a top priority for AI teams
• Frameworks and practices for identifying and managing AI risk 
• The growing importance of automation for effective governance
• Fairness, business, and other metrics for AI governance
• How to govern an Agentic AI future 

This episode is a collaboration with the Ethical Machines podcast featuring Reid Blackman — CEO of the AI ethical risk consultancy Virtue — and Nick Elprin, cofounder and CEO of Domino Data Lab.

Join us as they discuss:

• The differences between AI ethics and AI governance
• How the AI governance challenges have changed (and where they haven’t)
• The people, process and technology solutions for genAI governance (and how they are all intertwined)
• Predictions about AI maturity, slip ups and regulation

Innovation and AI governance aren’t at odds. Done properly, governance practices can be the key to accelerating implementation of even the latest GenAI use cases. In this interview from RevX London with Raj Mukherjee, Head of Data Science and AI at Direct Line Group, we find out how they embraced the principles of a lean startup, adopted a product mindset and became the first major insurance company to implement a GenAI solution to accelerate customer service. Come find out how they were able to overcome the challenges of legacy infrastructure, build trust and do the seemingly impossible — make compliance fun.

What’s next for artificial intelligence in 2025? In this episode, Dr. Kjell Carlsson delivers his annual predictions about the immediate future of AI. From AI becoming the universal corporate scapegoat to the rebranding of generative AI as "agentic AI.” Discover why commercial AI solutions remain scarce, the pivotal role of workforce upskilling in achieving transformative AI outcomes, and how governance is shifting from regulation-focused to impact-driven.

Want to see how Kjell fared in his 2024 predictions? Look no further than Data Science Leaders podcast Episode 62: AI in 2024: Predictions on the Future of the AI Revolution.

AI is transforming drug discovery and clinical trials, making what was once cutting-edge the new standard. Pharma companies are evolving into tech companies, leveraging AI to revolutionize every phase of drug development.

In this episode, Chris McSpiritt, VP of Life Sciences Strategy at Domino, discusses how AI is no longer a niche capability for select firms but an essential capability for all biopharma organizations. We explore AI-based digital organisms, the increasing reliance on AI in clinical trials, and the shift toward personalized medicine.

Join us as we dive into:

• How AI will drive the majority of new drug discoveries
• The role of AI in clinical trials
• The growing need for pharma companies to adopt a tech company mindset
• The impact of AI-driven automation on regulatory compliance and reporting

AI is transforming financial services and insurance, but rarely in the headline-grabbing ways you might expect. From intelligent process automation to fraud detection and risk management, AI is being embedded across operations, but as AI adoption grows, so do the challenges—cybercrime, rapid technological changes, and ever-evolving regulatory frameworks.

In this episode we sit down with Adam Gale, Field CTO for AI & Cyber Security at NetApp, and Mike Upchurch, VP of Strategy for Financial Services and Insurance at Domino, to explore:

• The "silent revolution" of AI in finance
• Unlocking the potential of unstructured data
• AI governance for innovation and regulatory compliance
• Strategies for driving AI adoption
• Top predictions for AI in 2025 in FSI

What does the near term future of Enterprise AI look like? A scramble to get valuable, cost-effective, enterprise-grade AI solutions into production, with rigorous governance at scale. If that sounds like a tall order - that’s because it is! However, large advanced organizations (yes, even outside of the tech sector) are already seeing success deploying AI solutions and we can expect the pace to accelerate rapidly in the next 1-2 years.

To find out how enterprises are gearing up for this challenge we speak with Richard Swakla, AI and ML Specialist at NetApp and get the inside scoop on the trends that are underway and the strategies organizations are putting in place to operationalize meaningful AI outcomes.  
Join us as we discuss:

• The pressure to shift from experimentation to production
• Breaking down the data, infrastructure and organizational silos
• Best practices for tackling governance, security and cost challenges
• Examples of AI efficiency gains in healthcare, pharma, and beyond