Scott Zoldi, Chief Analytics Officer, FICO. Read Scott Zoldi's full executive profile here.
Last year, FICO decided to do something about it. My team and I devised an analytic challenge relevant to aspiring Black data scientists that would give them a taste of the real-world problems we work to solve in the financial services industry.
In cooperation with three HBCUs, we created an analytic problem that students could complete in about 12 weeks while concurrently learning about the principles and practices of responsible AI. This particular data science problem challenged students to identify and mitigate bias in housing datasets that are used to factor into lenders’ mortgage decisions. Enlisting key members of my data science team as mentors, we met and worked with more than 100 students, faculty and staff over the 2023 to 2024 academic year at HBCUs in Alabama, Maryland and Delaware. Throughout this process, I enjoyed one of the most gratifying experiences of my career and learned much about how companies can ignite students’ curiosity and intellectual passion.In general, HBCUs still lack the resources that other institutions have committed to their data science programs. Many data science programs at HBCUs are nascent but eager to grow, leveraging resources like the. Leaders at the three-member institutions approached to participate in the analytics challenge—Alabama Agricultural & Mining University, Bowie State University and Delaware State University—were excited to expose their students to this real-world exercise.As I worked through the program with the students, I consistently heard that what they liked best was having the unique opportunity to engage in rigorous analytic exercises beyond the classroom. For the first time, they could get their hands on real data—big, unwieldy, messy data—and grapple with real-world issues, which starts with removing bias in the dataset used to construct a model. That's the reality of data science—it isn't a clean, deterministic system and there's no single right answer. Instead, there are myriad decisions for data scientists to make, each of which incrementally impacts the quality or bias of the resulting model. As a corollary, given the collective complexity of these data science decisions, each one needs to be carefully tracked, no matter how infinitesimal it may seem. Analytic models are the eventual products of multiple paths that were explored during the development process and provide important pointers as to how data scientists can drive better outcomes from an ethical perspective. Teaching the students how to track their work served as a primer on one of the fundamental practices of The challenge introduced students to another real-world concept: the value of working with mentors to understand how data scientists build and train analytic models. In addition to my lectures and speaking with students in person at all the schools, senior data scientists on my team met with students weekly, helping them work through the challenge exercises. These"office hours" gave the students a sense of what a data science career could look like if they continued this professional direction. Best of all, the students and mentors forged genuine, mutually enriching connections.To be part of the process of developing the next generation of data scientists, companies of all kinds must act to pass the baton to upcoming data scientists and business leaders. Preparing for the next generation requires more than providing funding or internships. I’m convinced that being personally involved with the students, working through problems and experiencing the joy of learning is the most successful way to inspire students.
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