Design and implement causal inference frameworks and statistical models to measure the impact of interventions, evaluate system performance and uncover opportunities for improvement.
Build, evaluate and iterate on causal ML models that power high-stakes decisions, applying best practices across the full model lifecycle from feature engineering to production deployment.
Develop frameworks to analyze tradeoffs between competing objectives (accuracy, coverage, user experience and operational cost), and propose strategies to improve overall effectiveness.
Build strong relationships with cross-functional partners across Product, Design, Engineering, Operations, and Analytics to drive collaboration and innovation.
Communicate learnings to leaders and stakeholders in a clear, compelling manner that drives informed, data-driven decision-making.
Think strategically about how to scale and evolve data science capabilities within your domain, contributing to the long-term vision for how science drives platform outcomes.

2+ years of industry experience in a quantitative analysis role with a Master's degree in a quantitative field (statistics, economics, computer science, etc.), or PhD in relevant fields.
Strong knowledge of causal inference and experimental design.
Strong knowledge of Bayesian modeling and statistical inference.
Hands-on experience building and deploying statistical or ML models in production environments.
Skilled in statistical programming (Python/R) and database usage (SQL).
Proven ability to communicate clearly and effectively to audiences of varying technical levels.
Ability to translate complex findings into compelling narratives that drive impact.
Excellent project management, communication and collaboration skills.

This role may also be eligible for bonus, equity, benefits, and Employee Travel Credits.

Data Scientist – Inference, Community Support

Key skills