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 ..read more

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 tran ..read more

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 fe ..read more

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 worl ..read more

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 ..read more

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 scienc ..read more

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 d ..read more

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 ..read more

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 s ..read more

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 leade ..read more