Data Scientist, RegLab - Stanford Law School

Stanford University · Stanford

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This is for a ONE-YEAR FIXED TERM position with the opportunity for extension based on performance.
Multiple Positions will be Filled


*Budgeted Range: The Law School’s budgeted pay for or this position is: $93,163 - $104,477 per annum.


The Regulation, Evaluation, and Governance Lab (RegLab) at Stanford University is looking for a full-time Data Scientist to provide analytical expertise across our research programs.
 

About Us: Stanford RegLab builds the evidence base and technology for effective government. Our interdisciplinary team of engineers, data scientists, social scientists, and legal experts partners with agencies at every level—from federal departments to states, counties, and cities—bringing frontier AI, machine learning, and causal inference to the public sector. RegLab’s work has prompted an overhaul of tax auditing, mapped racial covenants across millions of records, and enabled streamlining of statutes and regulations.


You will:


Preferred Qualifications:


Nice to Haves:

How to apply:


There will be two rounds of application review. The deadline for the first round is 7:00AM PST on Friday, October 23, 2026. All applications received before this date are guaranteed to be read while there is a spot open. After this date we will still be accepting applications received by December 1, however preference will be given to first round applications.
 

Applicants with Optional Practical Training (OPT) temporary employment authorization are eligible to apply. Depending on the circumstances, Stanford may sponsor J-1 visas for this position.


There is a two-step process to be considered for this fellowship:

  1. Please submit your resume and cover letter when you apply through Stanford’s career site – reference job number: 201247
  2. Additionally, upload all of the following here https://reglab.stanford.edu/work-with-us/. 
Manage and analyze large amounts of information, which is typically technical or scientific in nature including creating large and complex data sets, abstracting and formatting data, importing and exporting data between files, performing statistical analysis, and preparing reports and manuscripts for publication.

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