Brian M. Bot

About Brian M. Bot

I am a passionate scientist, strategist, and open knowledge advocate working at the intersection of data, technology, and governance. This work is centered on creating a more just and equitable data ecosystem primarily through the development and implementation of ethical data practices across a wide range of stakeholders and sectors. I have been invited to share my experiences as an advisor to organizations such as the White House Office of Science and Technology Policy, the Bill and Melinda Gates Foundation, and the International Data Forum. Additionally, I was recognized by the Mozilla Foundation as one of the “50 People Who Are Making the Internet a Better Place” and received the 2020 Symbiont Award for sustained excellence in scientific data sharing. I enjoy solving problems with passionate people and for mission-driven organizations.I am currently Director of AI & Data Science Partnerships at Fred Hutch. Previously, I worked as a Data Strategy Consultant working on assignment for the Bill & Melinda Gates Foundation where I lead the Analytics Unit for their DAC program. and spent 11 years with Sage Bionetworks most recently serving as Chief of Staff and leading Outreach and Strategic Development on behalf of the Executive Leadership Team. And if we go in the wayback machine, I worked as a statistician at the Mayo Clinic for seven years where I focused on colorectal cancer, clinical trials, and high-throughput genomics.

Experience

1. Director, Ai & Data Science Partnerships

   Fred Hutch

   Mar 2024 — Present · Seattle, WA, US

   Successfully raised over $60M as part of the core development team that launched the Cancer AI Alliance (CAIA) to support federated learning across four comprehensive cancer centers by aligning with four foundational industry partners- Founding and Acting Executive Director for CAIA, leading it\'s Strategic Coordinating Center

Education

• University of Minnesota

  BS, Statistics

  2000 — 2003

Skills

• Cell Biology
• Biotechnology
• Oncology
• Genetics
• Data Analysis
• Clinical Research
• Machine Learning
• Biomarker Discovery
• Clinical Trials
• Statistics
• R
• Biochemistry
• Genomics
• Sequence Analysis
• Molecular Biology
• Computational Biology
• Biostatistics
• Bioinformatics
• Sequencing
• Data Mining
• Science
• Research
• Life Sciences
• Cancer