Senior Data Scientist, Managed Invest & Wealthsimple For Business
Hamilton, Ontario, Canada
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Summary
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Senior
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Top School
Daniel Witvliet is a Senior Data Scientist with a decade of experience applying rigorous experimental design, causal inference, and scalable analytics to product and monetization problems across fintech and edtech. Currently leading analytics for Managed Invest and Wealthsimple for Business, he has driven measurable revenue and payment success improvements through A/B testing, pipeline migrations to Databricks/dbt, and self-serve experiment tooling. His background bridges academic rigor—a PhD in Molecular Genetics and postdoctoral research at Harvard on brain networks—with product-driven impact at Coursera, where he led teams, improved pricing and payments, and established company-wide metric governance. An active technical editor for the well-known "Causal Inference for the Brave and True" handbook, he focuses on making complex methods accessible to practitioners. Based in Hamilton, Ontario, he combines interdisciplinary project leadership with hands-on data engineering and mentorship to move high-impact analytics into production.
10 years of coding experience
12 years of employment as a software developer
Bachelor of Science (B.Sc.) Molecular Biomedicine, Bachelor of Science (B.Sc.) Molecular Biomedicine at Københavns Universitet - University of Copenhagen
Data Scientist, Data Scientist at Dataquest.io
Doctor of Philosophy (Ph.D.) Molecular Genetics, Doctor of Philosophy (Ph.D.) Molecular Genetics at University of Toronto
Causal Inference for the Brave and True. A light-hearted yet rigorous approach to learning about impact estimation and causality.
Role in this project:
Technical Writer
Contributions:13 commits, 12 PRs, 1 comment in 23 days
Contributions summary:Daniel's commits primarily involve fixing typos, grammatical errors, and improving the overall English in the various chapters of the "Causal Inference for the Brave and True" handbook. They made changes to the text in multiple `.ipynb` files across different chapters, specifically focusing on refining the language used to explain complex concepts related to causal inference. The user’s contributions span almost all the chapters of the handbook. This indicates a significant effort to improve the clarity and readability of the educational materials.
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Daniel Witvliet - Senior Data Scientist, Managed Invest & Wealthsimple For Business