60 Seconds with Gina Papush

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Gina is currently the Chief Analytics Officer for Bristol Myers Squibb. In this role, she leads the organization's global team responsible for driving data analytics vision, strategic priorities, and delivery across the enterprise. Gina and her team leverage data, advanced analytics, AI/ML, and digital across all areas of the business, creating patient value and accelerating biopharma innovation.

Prior to joining Bristol Myers Squibb, Gina served in Global Chief Data & Analytics Officer roles for Cigna and QBE Insurance Group. She also held leadership positions in data and analytics within the financial servics and consutling industries, includeing at Citigroup, GE Capital, and Fulcrum Analytics. Her experience includes leading analytics, data science, data/information management, customer engagement, risk management, strategic business planning, and market research for retail and digital banking, consumer lending and credit, property and casualty insurance, health care, pharma, traditional and digital retail, manufacturing, and endtertainment industries.

Gina is active in advising on data analytics, AI, and digital as well as contributing to professional communities. She is a member of the board of directors at IAT Insurance Group, and an advocate for women in STEM through her advisory work with Women Business Collaborative and BMS Womens Resource Group. Gina holds an MS in Statistics from the University of Maryland, where she also completed her PdD qualifications.

What was your first car?

My first car was a Nissan Sentra hatchback with 2 doors. My uncle helped us buy it when we came to the United States, and we felt like we had a really cool car. We could pack the kids in the back, and they couldn't get out because of the two doors - they were very safely tucked in there.

What originally drew you to the risk management and investment fields?

I studied applied math and stats as an undergrad, doing computer science course work, and what was really interesting to me is being able to apply this into the real world and solve problems that would go beyond academic interest. So, when I contemplated graduate education, I chose statistics because I felt this was a field that was addressing needs across many different disciplines and was a way to connect into real world problem solving.

Who was the best mentor you ever had?

I had several and consider myself very fortunate. I have worked with many outstanding leaders during my time at Citi, including Marsha Tal, who founded the decision management and analytics function at Citi on the consumer side. And then at Cigna, I worked for a very terrific leaer and mentor, Matt Manders, who enabled me to contribute in ways that can broadly apply data and analytics across a large global healthcare services business. These and other leaders contributed immensely to what I was able to accomplish and helped shape me into who I am today.

What is the biggest risk that you see in the next 12 months?

In the data and analytics and AI space, our biggest risk is getting overexcited about the opportunities of the advancements in AI, which by no means are minor. I mean, we're really seeing a tremendouts opportunity to evolve in our space, but there is also a signivicant risk of overspending on things that will not actually create the meaningful impact or returns. So, balancing that is an important thing. Risks related to Governance all come with the use of new data technology, new ways to apply analytics, and AI. I think all of those are quite relevant in our space.

What field do you see as the most complementary to a career in risk management?

There are a few. I've worked with colleagues who had come from other fields or had transitioned into other fields. I think we have a very strong connectivity to digital and digital technologies - you really need data analytics and technology to work hand-in-hand to deliver on the promises of digital and AI. We also have incredible opportunities to impact strategy, product, and operations.

What's the most significant change you've seen in the industry over the course of your career?

This is fascinating, because in some ways we've experienced tremendous change, and it's amazing how far we have gone and what we're able to do today. Our ability to predict, our ability to analyze massive amounds of data and to build complex algorithms is just astounding in relation to what we were able to do 10 or 20 years ago. On the flip side of that, certain things haven't changed as much as we hoped. We still talk about challenges getting access to good data; about challenges with predictive accuracy of models; about challenges in terms of governing the appropriate uses of data. So, we are evolving along a spiral. If we were on the lower levels when I started in this field, we're now on the upper floors of this evolution curve. Yet, we're tackling some of the same foundational issues and need to continue the progress and apply the learnings we acquired along the way.