Jeffrey Edwards
Staff Data Scientist
Paris, Ile-de-France, United States
Summary
🤩
Rockstar🎓
Top SchoolJeffrey Edwards is a Staff Data Scientist based in Paris with five years of focused industry experience and a decade-plus track record leading analytics and marketing science teams. He has progressed from founding and running analytics firms and an e-commerce brand to senior roles at Meta and Google, blending hands-on modeling with product-facing impact. Jeffrey contributed backend improvements to Meta’s open-source Robyn MMM package, signaling deep familiarity with marketing mix modeling, adstock dynamics, and calibration logic. He combines rigorous predictive-analytics training from Northwestern with practical leadership as Head of Machine Learning and Head of Data Science roles, able to translate complex causal models into business decisions. Known for fixing core-model issues rather than surface tweaks, he brings a mix of entrepreneurial grit and enterprise-scale rigor to measurement and ML problems.
5 years of coding experience
11 years of employment as a software developer
Master of Science (M.S.), Predictive Analytics, Master of Science (M.S.), Predictive Analytics at Northwestern UniversityBachelor of Science (B.S.), Business Management, Bachelor of Science (B.S.), Business Management at Utah Valley University
French, English
Github Skills (3)
python10
econometrics9
hyperparameter-optimization8
Programming languages (2)
Github contributions (5)
facebookexperimental/Robyn
Dec 2021 - Mar 2022
Robyn is an experimental, AI/ML-powered and open sourced Marketing Mix Modeling (MMM) package from Meta Marketing Science. Our mission is to democratise modeling knowledge, inspire the industry through innovation, reduce human bias in the modeling process & build a strong open source marketing science community.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:36 commits, 17 PRs, 27 pushes in 2 months
Contributions summary:Jeffrey made changes to the `util/robyn.py` file, which is likely a core Python script within the marketing mix modeling package. The commit messages indicate that the user was working on core logic within the model, including objectives and calculations related to model decomposition, adstock, and calibration. Their contributions involved modifying existing code to address errors and make formatting adjustments. These changes suggest a focus on improving the model's functionality and accuracy.
aimachine-learningmarketing-mix-modelingmarketing-mix-modellingmmm
dataformed/howdiditdo
Sep 2022 - Sep 2022
Contributions:7 commits, 2 PRs, 5 pushes in 1 day
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