Data Scientist, RegLab - Stanford Law School

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:

• Work closely with the Faculty Director, Research Directors, Senior Data Scientists, and teams of fellows and students to drive forward a diverse research program focused on machine learning and policy evaluation
• Design, implement, and interpret the results of new experiments and studies.
• Work with large untapped data sets, such as: arge legal corpora, administrative records for public programs, LLM traces and benchmarks, health and environmental enforcement data
• Develop and devise state-of-the-art machine learning models, algorithms, and statistical models, while leading the collection of new data and the refinement of existing data sources.
• Devise methods for identifying data patterns, trends in available information sources using a variety of qualitative and quantitative techniques.
• Determine and recommend additional data collection and reporting requirements.
• Lead the implementation of data standards and common data elements for data collection.
• Work with self-initiated direction to assess and produce relevant, standard, or custom information (reports, charts, graphs and tables) from structured data sources by querying data repositories and generating the associated information. Write and distribute reports based on data analysis to applicable agencies, researchers, or other internal end-users.
• Serve as a resource for non-routine inquiries such as requests for statistics or surveys.
• Have the opportunity to receive co-authorship on research papers

Preferred Qualifications:

• A bachelor's degree, or MS or Ph.D., in a relevant quantitative field (e.g., data science, computer
  science, statistics, engineering, mathematics, economics, or a related field) and three years of (a) relevant professional experience or (b) combination of education and relevant professional experience
• Expert knowledge of programming languages (such as Python, R, and/or SQL)
• A deep understanding of modern statistical and machine learning models, when to apply them,
  and how to evaluate their performance
• Excellent written and verbal communication skills and a focus on achieving results
• Ability to work effectively with multiple internal and external customers, and ability to take a
  leadership role on projects and with users/clients.
• Self-guided, self-learner, and engaged in the mission of the Lab

Nice to Haves:

• Specialization in machine learning frameworks (TensorFlow, TF, PyTorch, Scikit Learn, etc.),
  NLP, LLM evaluation, computer vision, or related fields
• Academically-minded, with experience working in an academic setting

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/.

• Brief cover letter explaining your interest in the position
• Current resume or CV
• Transcript (unofficial version acceptable)
• Writing/code sample
• Contact information for no fewer than two academic references who can attest to your academic research skills and for any additional references who can speak to your character and professional skills

Core Duties:

• Collect, manage and clean datasets.

• Employ new and existing tools to interpret, analyze, and visualize multivariate relationships in data.

• Create databases and reports, develop algorithms and statistical models, and perform statistical analyses appropriate to data and reporting requirements.

• Use system reports and analyses to identify potentially problematic data, make corrections, and determine root cause for data problems from input errors or inadequate field edits, and suggest possible solutions.

• Develop reports, charts, graphs and tables for use by investigators and for publication and presentation.

• Analyze data processes in documentation.

• Collaborate with faculty and research staff on data collection and analysis methods.

• Provide documentation based on audit and reporting criteria to investigators and research staff.

• Communicate with government officials, grant agencies and industry representatives.

Minimum Education:

Bachelor's degree or a combination of education and relevant experience.

Minimum Experience:

Experience in a quantitative discipline such as economics, finance, statistics or engineering.

Knowledge, Skills and Abilities:

• Substantial experience with MS Office and analytical programs.

• Strong writing and analytical skills.

• Ability to prioritize workload.