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. Under review, with Aurélien Bibaut, Winston
Chou, and Nathan Kallus.
-
(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.