Simon Ejdemyr – Data Scientist

Things I do | Writing | Tutorials

I'm a data scientist and (self-proclaimed) engineer building tools for decision making and strategy. Since 2020, I've been at Netflix focusing on experimentation and observational causal inference.

My background is in computational social science. Although I use a range of quantitative methods in my work, I believe the best insights and predictions are formed when large-scale data and automation are combined with careful human judgement and curation.

I'm from Sweden 🇸🇪, where I enjoyed a short pro football (fine... soccer) career before going to the US for college and a PhD. I live in Los Angeles with my wife, son, and daughter. My last name is pronounced AY-duh-meer. 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:

(2024) Learning the Covariance of Treatment Effects Across Many Weak Experiments. Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 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.