Hello —
Data Scientist @ UCSF
I’m finishing an M.S. in Data Science at UCSF, where I work with the Vonk Lab on biomarker‑linked trajectories of cognitive decline.
Before grad school I spent three years at SSENSE in Montréal — first in localization, then as a data analyst building product analytics, KPI frameworks, and the dashboards teams use to decide. I write Python, SQL, and R, and I am most interested in problems where careful measurement changes a decision.
I work at the seam between behavioural data and the decisions a product team has to make — instrumentation, KPI frameworks, A/B testing, root‑cause investigation. Three years inside SSENSE taught me that the metric that gets defined is the metric that gets improved; that taste and measurement aren’t opposites; and that the most useful dashboard is the one a tired stakeholder still understands at 5pm on a Friday.
Today I’m at UCSF, modelling longitudinal language data with mixed effects, and in parallel building merchandising signals that surface emerging styles four to six weeks earlier than a moving‑average baseline. Outside of work I read about experimentation methodology, longitudinal models, and the slow craft of turning data into decisions.
A signal pipeline that watches forty‑plus brands week by week and flags emerging silhouettes before a moving average can.
A reproducible analysis stack that turns repeated‑measure language data into interpretable trajectories of cognitive decline.
statsmodels.MixedLM producing slope and gap contrasts for AoA, lexFQ, word count.SSENSE
Montréal, Canada
SSENSE
Montréal, Canada
University of California, San Francisco
M.S. Data Science STEM
Coursework: Python & R, Machine Learning, Bayesian Methods, Statistical Computing, Data Visualization, Computational Modeling.
McGill University
B.Sc. Nutritional Science STEM
Major Entrance Scholarship; Atlantic Provinces Scholarship.
Analytics & experimentation
Programming
BI & data
Workflow & cloud