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 Lab Experience working with machine learning frameworks (TensorFlow, TF, PyTorch, Scikit Learn, etc.), NLP, computer vision, or related fields is a plus 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.