Research Fellow, Reglab - Stanford Law School

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 a member of our research team, you will work closely with the Faculty Director, Research Director/Manager, data scientists, and teams of fellows and students to drive forward a diverse research program focused on machine learning and policy evaluation. The following tasks are designed to culminate in co-authorship on research papers: Plan and perform research tasks requiring initiative and judgment: conceptualize suitable empirical methodologies and models, Collect, manage, and structure quantitative datasets, Conduct statistical analyses of complex datasets and interpret results, Design and implement state-of-the-art machine learning models, algorithms, and statistical models, Participate in literature searches, report writing and manuscript preparation Present research findings to supervisor. Ensure compliance with research protocols. You may be asked to orient and train new staff or students.

Qualifications: A Bachelor's degree in an applicable quantitative field (e.g., computer science, data science, statistics, engineering, mathematics, economics, or a related field), MS degree preferred. Creativity Ability to quickly learn in a new work environment Eagerness to take initiative and solve intricate problems Ability to work under deadlines with general guidance. Exceptional research, analytical writing, and communication skills Excellent organizational skills and demonstrated ability to complete detailed work accurately. Programming experience in R, Python, Stata, SAS, and/or other languages Prior research experience and coursework in the empirical social sciences is preferred, but not required Self-guided, self-learner, and engaged in the mission of the LabExperience working with machine learning frameworks (TensorFlow, TF, PyTorch, Scikit Learn, etc.), NLP, computer vision, or related fields is a plus

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, 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: 201228 Additionally, upload all of the following:
• Brief cover letter explaining your interest in the position and which graduate degree(s), if any, you hope to pursue following the fellowship
• Current resume or CVTranscript (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: Plan and perform research tasks requiring initiative and judgment by applying basic knowledge and understanding of scientific theory when precedents do not provide specific guidance. General instruction provided by the supervisor as needed. May interpret study results in collaboration with supervisor or PI.Participate in the development and administration of survey instruments and rating scales requiring judgment in applying non-routine procedures. Analyze and summarize results for review with supervisor. Audit the accuracy and validity of data Review and audit case report forms for completion and accuracy with source documents, and ensure compliance with research protocols. Identify, select, extract and summarize data and structured information. Present summary of findings to supervisor. Conduct literature searches, and write literature summaries and manuscripts, requiring preliminary judgments after the supervisor outlines conceptual approach. Build and organize data as requested by principal investigator or supervisor; use common statistical programs requiring the application of job control language in generating and organizing data. Adapt new, nonstandard methods outlined by supervisor in designing and evaluating phases of research projects, (i.e., educational materials, questionnaires, strategies for recruitment, data quality control procedures and processes). May follow up with Institutional Review Board (IRB) to ensure renewals are approved and completed, seeking guidance where necessary. Assist with development, communication and design of research findings to internal and external audiences, which may include web updates, social media, and/or white papers, for use in recruitment, educational, or awareness of programs, with guidance from supervisor. May orient and train new staff or students.

Minimum Education and Experience: Bachelor of Arts degree in an applicable social science related field, or combination of education and relevant experience in an applicable social science.

Knowledge, Skills and Abilities: General understanding of scientific theory and methods, typically gained through completion of an undergraduate degree in a related field. General computer skills and ability to quickly learn and master computer programs. Ability to work under deadlines with general guidance. Excellent organizational skills and demonstrated ability to complete detailed work accurately. Effective oral and written communication skills. Ability to work with human study participants.