Requirements

Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field, or equivalent practical experience.
8 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 6 years of work experience with a PhD degree.

Nice to haves

PhD degree in a quantitative discipline (e.g., Statistics, Operations Research, Economics, Computational Biology, Computer Science, Mathematics, Physics, or Engineering).
Experience with statistical software (e.g., Python, R), database languages (e.g., SQL), and agentic development (e.g., Gemini, Antigravity).
Demonstrated statistical data analysis and experimental design capabilities.
Deep expertise in modern statistical theory, including experiment design, regression models, causal inference, sampling methods, time-series analysis, and hierarchical modeling.
Strong record of scientific communication and presentation skills, with the ability to distill investigative findings for executive stakeholders.
Strong data intuition and business acumen, with relevant experience in data analysis to solve business problems in complex, fast-moving, and ambiguous business environments.

Collaborate with engineering and product partners to identify key questions and translate and refine business questions into experiments, analysis, evaluation metrics, or mathematical models.
Architect end-to-end analysis pipelines, translating ambiguous business questions into rigorous frameworks and statistical models.
Design and evaluate complex experiments or models (e.g., causal inference, hierarchical models) to tackle problems with limited precedent in retrieval and ranking.
Translate system telemetry into statistically sound evidence, ensuring highest standards of data integrity for research-grade analysis supporting billion-dollar launches.