Simon Ejdemyr – Data Scientist

Things I do | Writing | Tutorials

I'm a data scientist and (self-proclaimed) engineer, working on causal inference, statistical modeling, and tools that support better decision making under uncertainty. Since 2020, I've been doing this work at Netflix.

My background is in computational social science. I use a range of quantitative methods, but I've found the best insights come when large-scale data and automation are paired with careful human judgment and domain knowledge.

I'm from Sweden 🇸🇪, where I enjoyed a short pro football (fine... soccer) career before moving to the U.S. for college and a PhD. I now live in Los Angeles with my wife, son, and daughter. Feel free to connect on LinkedIn.

Things I do

Build products and tools to make sense of complex data
Design systems for randomized controlled trials and observational causal inference
Develop, prototype, and productize machine learning and inference models based on analytical solvers or (Bayesian) probabilistic programming
Use Python, R, SQL, bash, Stan, and more; and contribute to and design APIs
Enjoy teaching and mentoring (in 2016 I won Stanford's Centennial Teaching Award)

Writing

A few things I've published in academia and industry:

(2025) Evaluating Decision Rules Across Many Weak Experiments. Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '25), with Winston Chou, Colin Gray, Nathan Kallus, and Aurélien Bibaut. Best Paper Award, Applied Data Science. [Blog Post]
(2025) Optimizing Returns from Experimentation Programs. ACM Conference on Economics and Computation (EC '25), with Timothy Sudijono, Apoorva Lal, and Martin Tingley.
(2024) Learning the Covariance of Treatment Effects Across Many Weak Experiments. Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '24), with Aurélien Bibaut, Winston Chou, and Nathan Kallus. [Blog Post]
(2024) Estimating the Returns from an Experimentation Program. MIT CODE, with Martin Tingley, Yian Shang, and Travis Brooks.
(2023) Long-Term Causal Inference with Imperfect Surrogates using Many Weak Experiments, Proxies, and Cross-Fold Moments. MIT CODE, with Aurélien Bibaut, Nathan Kallus, and Michael Zhao.
(2022) A Framework for Generalization and Transportation of Causal Estimates Under Covariate Shift. MIT CODE, with Apoorva Lal and Wenjing Zheng.
(2021) Decision Making at Netflix. Netflix Tech Blog, with Martin Tingley and others.
(2020) Low-latency Multivariate Bayesian Shrinkage in Online Experiments. MIT CODE, with Matthew Wardrop and Martin Tingley.
(2019) 4 Keys to Using Machine Learning for Campaign Measurement. Facebook IQ blog.
(2018) Do Elections Improve Constituency Responsiveness? Evidence from U.S. Cities. Political Science Research and Methods, with Darin Christensen.
(2017) Segregation, Ethnic Favoritism, and the Strategic Targeting of Local Public Goods. Comparative Political Studies, with Eric Kramon and Amanda Lea Robinson.
(2015) Global, Regional, and National Levels and Trends in Under-5 Mortality Between 1990 and 2015, with Scenario-based Projection to 2030. The Lancet, with UNICEF colleagues.

Tutorials

At Stanford I taught classes in applied statistics, for which I developed a number of R tutorials. While likely out of date, I've heard some are still useful.