Research Scientist, Demography and Survey Science

Key Skills

Java Programming, Python Programming, SQL, Apache Hadoop, Data Modeling, Apache Hive, R Programming, Statistics, Stata, Large scale data processing, Data Analysis

Job Description

• Develop a research agenda and drive research projects from end-to-end
• Collaborate with cross-functional teams across the company to define relevant research questions
• Design and deploy appropriate research methodologies to answer those questions
• Develop innovate new approaches to resolve challenging problems
• Provide teams with usable measurement strategies and methodologies to make better decisions
• Deliver insights and recommendations clearly to relevant audiences

Minimum Qualifications

• Demonstrated experience designing original research to address complex questions
• Experience initiating and driving research projects to completion with minimal guidance
• Experience communicating analyses and results to both technical and non-technical audiences
• Master’s in the social sciences (e.g., Economics, Political Science, Sociology, Psychology, Communication), or in a quantitative field (e.g., Statistics, Informatics, Econometrics) with experience answering social questions
• Skilled in at least three of the following: statistical inference, survey methodology, regression modeling, exploratory data analysis, and experimental design
• Knowledge of statistical analysis and programming languages (such as R/Stata, Python, SQL/Hive)
• Must obtain work authorization in country of employment at the time of hire, and maintain ongoing work authorization during employment

Preferred Qualifications

• Ph.D. in the social sciences (e.g., Economics, Political Science, Sociology, Psychology, Communication), or in a quantitative field (e.g., Statistics, Informatics, Econometrics) with experience answering social questions
• Experience with large data sets and distributed computing (Hive/Hadoop)
• Experience with Bayesian statistical models
• Experience with primary data collection
• Experience with survey sampling and weighting
• Experience with observational causal inference (e.g., regression adjustment, matching, propensity score stratification), or quasi-experimental methods (e.g., instrumental variables, regression discontinuity, interrupted time series)
• Familiarity with qualitative research (e.g., focus groups, interviews, cognitive walkthroughs