Data Scientist

The Center for Guaranteed Income Research (CGIR) is an unconditional cash-transfer research center headquartered at the
School of Social Policy & Practice at the University of Pennsylvania. CGIR conducts applied cash-transfer studies and pilot designs in concert with community-based organizations and government stakeholders to add to the empirical scholarship on cash, economic mobility, and poverty. CGIR is currently conducting the world’s largest multi-site evaluation of nearly 40 guaranteed income pilots across the nation as part of The American Guaranteed Income Studies (AmGIS). Using a common research design and a core survey across its pilot sites, the aims of AmGIS are to (1) facilitate comparisons across pilot sites, and (2) conduct analyses using a singular master dataset pooling data from more than 19,000 study participants across treatment and control groups. In addition, CGIR is augmenting its original survey data with administrative data from across its diverse pilot sites.

This position will provide statistical support to research projects at CGIR including, but not limited to, clinical trials, observational studies, and analysis of administrative records. This position will: assist study investigators in the areas of protocol development, study design, analysis plan, sample size / power calculations, and interpretation of study data; develop, design and write new and/or revised statistical programs and modeling based on requirements of ongoing research projects; conduct and interpret analyses of research data using descriptive statistics, basic inferential tests, and advanced statistical procedures; evaluate and ensure statistical integrity, accuracy and validity of research data; compile results and provide statistical summaries and reports to principal investigators; assist with grant proposal preparation in study design, sample size calculations, and development of statistical analysis plans; assist with preparation of journal articles, technical reports and other materials for publication and presentation; manage databases for assigned research projects; read and review appropriate statistical papers; and attend project meetings.

Bachelor's and 3 - 5 years experience required. Master's degree and 1-3 years of professional work experience in biostatistics, statistics, economics, mathematics or a related quantitative field in the human or social sciences with considerable knowledge and command of at least one of the following programming languages: Python, SAS, R or Stata, required or equivalent combination of education and experience prefered. Python and experience with unstructured or primary data strongly preferred. At least one year of professional experience as a biostatistician, or a similar role, in managing large datasets and in utilizing statistical software packages required; experience preparing grant applications and writing for peer-review preferred. Additional coursework in epidemiology, public health, social work, social policy, and economics considered a plus. This is a hybrid position with a requirement of 2-3 days per week in-person on campus.

Job Responsibilities

• Conduct statistical analyses including regression modeling, hypothesis testing, and results interpretation; implement advanced missing data imputation using machine learning methods; develop and maintain comprehensive data documentation, codebooks, and variable dictionaries; conduct systematic data quality checks and validation procedures.
• Manage analytic segments of research projects; collaborate with research teams by executing the analytic plans designed by the Principal Investigators and Senior Data Scientist according to the SOWs, by ensuring analytic plans are consistent with Co-PIs and by supervising 2-3 student workers when necessary; support regulatory compliance activities including data security procedures. This includes drafting data security workflows, implementing and monitoring them.
• Prepare reports of research findings and articles for white papers and peer review; present findings to diverse stakeholders; translate results for non-technical audience.
• Prepare reports of research findings and articles for white papers and peer review; present findings to diverse stakeholders; translate results for non-technical audience.