Data Science Leaders

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

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.

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


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.

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.

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

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


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.

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

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 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

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

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

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

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.

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 

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.

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

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.