Data Scientist – Employee Research

General Motors•Mountain View, CA

23h•$106,000 - $163,000•Remote

About The Position

The Data Scientist – Employee Research applies organizational research, behavioral science, and advanced analytics to study employee experience, engagement and sentiment, manager and team effectiveness, organizational health, workforce dynamics, and the impact of people interventions. The role sits within People Analytics and partners with Employee Voice, People Analytics Consultants, HR Business Partners, COE leads, and business leaders. The Data Scientist leads research projects from question formulation and study design through analysis, recommendations, and evaluation. The role develops reusable research methods and insights and partners with the solutions team on the production ownership of recurring data products, models, and platforms.

Requirements

Experience in data science, quantitative research, people analytics, organizational research, behavioral science, or a related field.
Strong Python or R skills and proficiency in SQL for data wrangling, statistical analysis, and reproducible research.
Expert knowledge of regression, hypothesis testing, survey methodology, psychometrics, experimental and quasi-experimental design, causal inference, and longitudinal or multilevel analysis.
Experience leading end-to-end research or analytical projects, from problem formulation through communication of findings.
Ability to explain complex analyses to non-technical audiences and exercise sound judgment with sensitive employee data.

Nice To Haves

Advanced degree or equivalent applied research experience in Industrial-Organizational Psychology, Organizational Behavior, Behavioral Science, Sociology, Economics, Statistics, Data Science, Psychometrics, or a related field.
Experience with employee surveys, engagement and sentiment, performance, movement, retention, talent, workforce planning, or lifecycle data.
Experience with intervention evaluation, longitudinal or panel analysis, multilevel modeling, mixed methods, text or NLP analysis, or Organizational Network Analysis.
Experience with workforce planning, scenario modeling, organizational effectiveness, manager and team research, organizational development, or change evaluation.
Demonstrated ability to shape research questions, influence HR or business leaders, and translate evidence into responsible action.

Responsibilities

Frame organizational questions, develop hypotheses, define outcomes, and identify the decisions research should inform.
Design and execute quantitative and mixed-method studies using survey, cohort, longitudinal, multilevel, experimental, quasi-experimental, and causal methods.
Integrate employee, HRIS, talent, performance, movement, operational, open-text, interview, and focus-group data where appropriate.
Study drivers and consequences of employee experience, including team, manager, workforce, and organizational factors.
Conduct driver, segmentation, trend, and linkage analyses to explain engagement and sentiment and identify actionable factors as well as relative business impact.
Evaluate people programs, manager initiatives, and organizational changes by defining outcomes, baselines, comparison groups, and follow-up measures.
Contribute to workforce research and planning through scenario analysis, workforce-risk indicators, and analysis of workforce supply, demand, capability, and movement.
Establish foundational use cases, data requirements, methods, and governance for Organizational Network Analysis and related research on collaboration, influence, connectivity, and change.
Assess emerging methods, including text analytics and artificial intelligence, when they address a defined research or business need.
Translate findings into clear recommendations, decision options, and measures of progress for technical and non-technical audiences.
Produce research briefs, executive presentations, evaluation readouts, measurement frameworks, and analytical documentation.
Partner with stakeholders to shape questions, challenge assumptions, and connect evidence to action.
Maintain reproducible, well-documented work through version-controlled code, transparent assumptions, quality checks, and clear limitations.