Facebook Academic Collaborator Quantitative Research - Social Impact 

United States, California 

93967091

Develop methodology to assess online and offline equity impact of products

Work closely with teams to identify research topics

Conduct research using a variety of quantitative methods

Design studies that explore unmet needs as well as current behavior and attitudes

Generate insights to fuel ideation, evaluate designs and guide product roadmaps

Work cross-functionally with product management, analytics, content design, product design, engineering, and marketing to communicate results to various audiences and integrate measurement strategy into various functions’ work streams

Externalize internal research and product development processes and approaches related to Equitable product development

MS/PhD in the social sciences

Demonstrated experience in quantitative research methodologies (e.g., survey sampling and design, regression modeling, experimental design, behavioral data analysis, causal inference with observable data, causal impact evaluation)

Experience using SQL/Hive, Python, and/or R to analyze large datasets (> 1TB)

Familiarity with machine learning, specifically content classification methods

In-depth understanding in assessing structural and systemic biases in science and technology, and a demonstrated experience in understanding of identity considerations as they relate to measurement.

Evidence of interest in exploring the impact of new technologies and offline societal systems.

Experience establishing and applying offline equity measurement, including experience measuring real-world impact of equity efforts (within or outside of the technology space).

Experience identifying and working with vendors on end-to-end research design and execution

While the disciplinary specialization for this position is open, we are particularly interested in candidates with backgrounds in assessing societal inequities and whose research and practice transcends disciplinary boundaries, including but not limited to: algorithmic inequality, critical algorithm or data studies, decolonization and postcolonial studies, ethics and politics of computing, ethnic studies, history, political science, science and technology studies, sociology, racial capitalism and platform studies, information technologies, or other related fields.