Data Science Leaders

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 teamsTune 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 rightTune 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.

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 projectTune 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.

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 roofHow data science helped keep Americans connected when COVID-19 drove massive shifts in internet usageDifferent ways to approach embedding data science into production systemsTune 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 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 organizationsTune 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 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 reasonsFor 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.

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 analyticsTune 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.