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Senior Staff Applied Scientist at Coinbase
Currently I'm a Senior Staff Applied Scientist at Coinbase working to modernize our risk and fraud infrastructure with machine learning and improved experimentation. Previously I was the Chief Scientist and Co-Founder at Insulearn where I focused on developing novel mathematical models for AID (Automated Insulin Delivery), leveraging my over 15 years of experience as a mathematician and applied scientist. Previously I was a staff data scientist at Lyft focusing on causal inference and experimentation, where I developed novel algorithms to improve rider and driver experiences, and manage our complex two-sided marketplace. I also lead our competitive marketshare team, where I drove organizational changes that lead to stabilizing and improving our competitive position over several years. Previously I completed my PhDs at the Courant Institute (NYU) and UPMC (Paris VI) where I worked on problems in energy driven pattern formation, variational calculus, non-linear PDE and dynamical systems. I worked full time as a research-only fellow and instructor of mathematics at DPMMS at the University of Cambridge prior to leaving academia.
Pierre and Marie Curie University
PhD, Applied Mathematics
January 1, 2010 – January 1, 2013
NYU Courant Institute School of Mathematics, Computing, and Data Science
Doctor of Philosophy (PhD), Mathematics
January 1, 2009 – January 1, 2013
École normale supérieure de Lyon
Mathematics
January 1, 2008 – January 1, 2009
University of Toronto
M.Sc, Mathematics
January 1, 2007 – January 1, 2008
University of Toronto
B.Sc, Mathematics, Engineering Physics
January 1, 2003 – January 1, 2007
Coinbase
Senior Staff Applied Scientist - Risk ML Tech Lead
February 1, 2026 – Present
Coinbase
Staff Applied Scientist
January 1, 2025 – February 1, 2026
InsuLearn
Chief Scientist
March 1, 2024 – Present
New York, United States
Lyft
Staff Applied Scientist & Technical Lead
August 1, 2018 – March 1, 2024
New York, United States
Columbia University in the City of New York
Adjunct Professor of Data Science
January 1, 2017 – August 1, 2019
R 7:00pm-9:30pm 214 Seeley W. Mudd Building
New York Times
Data Scientist
January 1, 2015 – October 1, 2016
New York City Metropolitan Area
The Data Incubator
The Data Incubator - Fellow
September 1, 2014 – October 1, 2014
Greater New York City Area
University of Cambridge
Herchel Smith Research Fellow and Instructor of Pure Mathematics
September 1, 2013 – December 1, 2014
London, United Kingdom
NYU Courant Institute of Mathematical Sciences
Postdoctoral researcher
May 1, 2013 – August 1, 2013
New York
Courant Institute of Mathematical Sciences (New York University)
Ph.D Student
September 1, 2011 – May 1, 2013
New York
The University of Bonn
Teaching Assistant, Partial Differential Equations
January 1, 2009 – June 1, 2009
University of Toronto
Teaching Assistant, Linear Algebra II
January 1, 2008 – June 1, 2008
University of Toronto
Teaching Assistant, Calculus 1
September 1, 2007 – June 1, 2008
University of Toronto
Teaching Assistant, Calculus 1
September 1, 2006 – June 1, 2007
Met Office
Researcher
May 1, 2006 – August 1, 2006
Exeter, United Kingdom
Universal Poker
Programmer/Owner
June 1, 2004 – June 1, 2006
Toronto, Canada Area
Resident Advisor Recommendation Engine
September 1, 2014 – Present
Developed a recommendation engine for the electronic music event website Resident Advisor (http://www.residentadvisor.net). Using scraped data from their website and collaborative filtering, I developed a nearest neighbors algorithm which provides suggestions for music events based on the users history. This combined with my ticket notification system allows users to easily find the events they are interested in and be notified as soon as tickets become available.
Cultural Fit Analysis
The candidate's diverse experience across academia, startups (InsuLearn), and large tech companies (Lyft, Coinbase, New York Times) suggests adaptability and a broad perspective. Their involvement in strategic leadership roles and teaching indicates a collaborative and knowledge-sharing mindset. The transition from pure mathematics to applied science and engineering roles demonstrates a strong drive for practical impact and continuous learning, which aligns well with innovative tech cultures.
Soft Skills & Operational Fit
The candidate demonstrates strong leadership, strategic thinking, and problem-solving skills through their roles as a Tech Lead and Chief Scientist. Their experience in driving company-wide priorities and optimizing user experience suggests a proactive and results-oriented operational fit. The academic background also points to a high degree of intellectual curiosity and rigor.