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Senior Data Scientist, RegLab - Stanford Law School

Stanford University • Stanford, CA • 1h ago

Job Description

Full Time

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: $117,134 -

$130,006 per annum.

The Regulation, Evaluation, and Governance Lab (RegLab) at Stanford University is looking for

a Senior Data Scientist to help drive forward a diverse research program focused on public sector

artificial intelligence (AI) applications and policy. We are looking for someone who has

excellent technical skills, strong research expertise, good communication skills, and is motivated

by the mission of the lab.

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.

As Senior Data Scientist, you will:

● Work closely with the Faculty Director, research leadership, and the rest of the RegLab

team to explore, identify, and implement research projects.

● Lead the design and development of multiple projects using state-of-the-art AI models,

algorithms, statistical models, and other programs designed to improve the public sector.

● Work with large untapped data sets, such as: health and environmental enforcement data,

mass adjudication records, high-resolution satellite imagery, and the largest publicly

available corpus of legal text.

● Mentor and/or manage other data scientists and junior researchers.

● Have the opportunity to receive co-authorship on research papers.

Core Duties:

● Collaborate with interdisciplinary research teams and external stakeholders to identify

high-impact opportunities for applied data science in the public sector.

● Engage with partners to understand their data environments, help shape project goals, and

ensure that technical outputs align with real-world needs and constraints.

● Adapt and refine existing AI models to align with the constraints and ethical

considerations of public sector use cases, including fairness, transparency, and

interpretability.

● Develop and test proto@type software and participate in the approval and release process

for new software.

● Prioritize and extract data from a variety of sources such as reports, legal documents,

notes, survey results, and satellite imagery, and maintain its accuracy and completeness.

● Determine additional data collection and reporting requirements.

● Create complex charts and databases, perform statistical analyses, and develop graphs

and tables for publication and presentation.

● Serve as a resource for non-routine inquiries such as requests for statistics or surveys.

● Provide documentation based on audit and reporting criteria to investigators and research

staff.

● Communicate findings and technical insights clearly to both technical and non-technical

audiences through reports, visualizations, presentations, and academic publications.

Preferred Qualifications:

● A bachelor's degree (MS or Ph.D. preferred) in a scientific or analytic 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.

● Strong programming skills in Python or a similar language, including debugging and

writing maintainable code

● Solid grounding in machine learning, NLP, and information retrieval methods, and

experience applying them to large or complex datasets

● Experience with data modeling and database systems for both structured and unstructured

data (e.g., SQL, document or vector stores)

● Experience building reliable, scalable data pipelines with attention to data quality and

validation, including evaluating new tools and frameworks

● Understanding of security best practices for web applications and backend systems, such

as authentication, access control, and handling sensitive data

● Excellent written and verbal communication, including documenting systems, decisions,

and recommendations for technical and non-technical audiences

Nice to Haves:

 Specialization in machine learning frameworks (TensorFlow, TF, PyTorch, Scikit Learn,

etc.), NLP, LLM evaluation, LLM-assisted workflows, computer vision, or related fields

 Experience with cloud infrastructure and compute resources (e.g., GCP Cloud Run,

Cloud SQL), including recommending tech stacks for research projects

 Familiarity with deploying containerized web applications (e.g., Docker, Cloudflare

Pages), such as results dashboards or validation interfaces for partners to review LLM

pipeline outputs

How to apply:

There will be two rounds of application review. The deadline for the first round is 7:00AM PST

on Friday, October 30, 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:

Please submit your resume and cover letter when you apply through Stanford's career

site - reference job number: 201360

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

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