Microsoft
Research Scientist - Economist
$102K–$219K
Redmond, WA +1 · On-site · Full-time · Posted Oct 30, 2026
- Location
- Redmond, WA
Mountain View, CA - Experience
- Minimum: 1+ year(s) with a relevant Master's degree, or 2+ years with a relevant Bachelor's degree, or equivalent experience. Preferred: Doctorate, or Master's degree and 3+ years of related research experience, or Bachelor's degree and 5+ years of related research experience, or equivalent experience.
- Education
- Relevant Master's or Bachelor's degree, or equivalent experience. A doctorate in a relevant field is preferred.
About the job
Conduct academic-quality economic research on high-impact business questions, while developing an independent research agenda and contributing to team projects.
What you'll do
- Drive an independent research agenda and collaborate with economists and data scientists on high-impact projects.
- Research topics such as advertising effectiveness, market design, pricing, demand estimation, and cloud marketplaces.
- Apply empirical and theoretical economic methods to produce research with real-world impact.
What they ask for
- Master's Degree in a relevant field and 1+ year(s) of related economic research experience, or Bachelor's Degree in a relevant field and 2+ years of related economic research experience, or equivalent experience.
- Proficiency in R or Python, including tools for data manipulation, graphing, and causal inference.
- Knowledge of standard econometric methods for small- and big-data settings.
- Experience publishing academic papers as a lead author or essential contributor.
Nice to have
- Experience participating in a top conference in a relevant research domain.
- Experience with Spark or other big-data processing tools, or developing custom empirical models and methods for pricing and/or market design.
Pay
USD $102,100 - $202,200 per year (base salary); USD $133,800 - $219,200 per year (base salary)
Tools
R, Python, dplyr, pandas, polars, Spark
AI focus
Research involving machine learning for prediction and causal inference, including methods such as double machine learning.
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