Kerstin Frailey, PhD, is a data and product leader passionate about building practical and purposeful technology through AI and machine learning. She focuses on AI business strategy, data quality, and impact-driven solutions. She has led teams across industries and at all stages of growth, from startups to post-IPO companies, including Asana, Numerator, and Olive AI. She is a former founder and a forever builder.

She holds a BA from Yale, two master’s degrees in Mathematics with concentrations in Computer Science and Statistics from the University of Illinois at Chicago, and a PhD in Statistics from Cornell. She was a Fellow at the University of Chicago and a member of the Pear Female Founders Circle.

She lives in the Bay Area with her two small dogs and a forest of houseplants.

Education History

  • B.A., Psychology, Yale University
  • M.Sc., Mathematical Computer Science, University of Illinois at Chicago
  • M.Sc., Mathematical Statistics, University of Illinois at Chicago
  • Ph.D, Statistics, Cornell University

Publications

Practical Data Quality for Modern Data and Modern Uses, With Applications to America’s COVID-19 Data ProQuest, 2023

Blogposts
The Impact Hypothesis: The Keystone to Transformative Data Science Metis Blog, 2019
Rabbit Holes, Red Herrings, and Rewards: Managing Curiosity Metis Blog, 2019

Presentations and Talks
AI in Industry Gen Ai X Summit at ODSC West, 2024 link
Less Data Needed: Why Data Selection is Critical to the Future of AI in Education AI in Learning Summit, 2023 link
Women Data Leaders Panel Discussion Data Leaders USA, 2023
The Stuff They Didn’t Teach You in Data Science Class Open Data Science Conference (ODSC) East, 2023 link
Breaking into Data Promotable, 2022 link
Chasing Impact GET Cities Kickoff Summit, 2021 link
Essential Data Literacy Demystifying Data Science Conference, 2019
On AI ROI: The Questions You Need to Be Asking Accelerate AI West, 2019 link
Building an Effective Data Science Project Portfolio Accelerate AI East, 2019 link
The Revolution Will Be Data-Driven ARNOVA, 2016 link
In Context: Finding Ourselves and Each Other through Data Do Good Data, 2016