Data Science Leaders

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 scienceTune 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.

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 teamTune 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 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 healthcareWorking with clinical analytics to root out biasAdvice for operationalizing 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.

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 casesTune 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.

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 onTune 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.

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 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.

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 scratchThe complexities of marketing analyticsThe value of continuous learningTune 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 teamsCheck out these resources mentioned during the show:- Analytics: How to Win With Intelligence- Building Analytics 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.

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 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.

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 HRData science in the hiring processEmbedding data science into SaaS platformsTune 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.