Research Associate

Required education and experience:

A master’s degree in a quantitative social science or related field (e.g., public health, sociology, economics, statistics, data science, or an interdisciplinary or related field with a strong quantitative component) AND

At least three years of full-time quantitative social science or public health work experience

Demonstrated experience compiling and analyzing quantitative data, including analyzing large public datasets

Demonstrated project management skills, including coordinating timelines and stakeholders

Natural attention to detail and drive for order that yields accuracy and reliability in the data and analysis you work with

Annual salary range: $80,000 - $90,000, depending upon experience and credentials. Grant-funded position with a one-year initial appointment; continuation is contingent on funding and performance. Comprehensive benefits include health, dental, vision, disability, life, and gym reimbursement; annual tuition and/or student loan reimbursement; pension plan and tax savings programs; generous vacation and sick leave; and more.

Location terms:

Candidates should be present in the New York metro area upon start of employment and have the ability and desire to work in-person at the SSRC office in Midtown three days per week.

Lead quantitative research on well-being, human flourishing, and access to opportunity (and related equity/public health/social and economic research topics)

Own end-to-end analytic workstreams for high-visibility projects—from research design and data acquisition through cleaning, analysis, QA/reproducibility, and integration into reports and interactive tools

Develop and communicate quantitative insights through clear data visualizations, written summaries, presentations, and web-based outputs (e.g., mapping tools/pages), in collaboration with a small research team and external technical and design contractor

Acquire, compile, and clean datasets from diverse public and private sources (especially U.S. Census/ACS and public health authorities)

Apply appropriate statistical methods to large datasets (e.g., descriptive analysis and hypothesis testing) and document methodological choices and tradeoffs

Build and maintain indicators/indices along with documentation, data-quality validation to ensure transparent, replicable outputs