Data Scientist - Inference, Community Support

About This RoleAI processing…

Key Responsibilities

• 1
  Design rigorous experiments & quasi-experiments to measure the causal impact of CS product launches and drive data-informed launch decisions.
• 2
  Build causal ML models to optimize Make Goods budget allocation and maximize business impact.
• 3
  Conduct causal inference analyses to quantify the long-term effects of product changes and uncover heterogeneous treatment effects.
• 4
  Deliver strategic insights on quality-cost tradeoffs, empowering leadership to deliver the best possible support experience to our community.
• 5
  Inference & Measurement: Design and implement causal inference frameworks and statistical models to measure the impact of interventions, evaluate system performance and uncover opportunities for improvement.
• 6
  Modeling: 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
• 7
  Optimization: Develop frameworks to analyze tradeoffs between competing objectives (accuracy, coverage, user experience and operational cost), and propose strategies to improve overall effectiveness.
• 8
  Collaborate Cross-Functionally: Build strong relationships with cross-functional partners across Product, Design, Engineering, Operations, and Analytics to drive collaboration and innovation.
• 9
  Influence Decisions: Communicate learnings to leaders and stakeholders in a clear, compelling manner that drives informed, data-driven decision-making.
• 10
  Empowerment: 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.

Requirements

• 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.

Perks & Benefits