Data Scientist - Inference, Community Support

Airbnb

13h•$151,000 - $175,000•Remote

About The Position

Airbnb is seeking a motivated and talented Data Scientist with strong causal inference expertise to join the Community Support Data Science team. This role will collaborate with engineers, product managers, designers, and operations agents to enable personalized, fair, and exceptional experiences for guests and hosts using advanced causal inference analysis for Community Support. The position is US - Remote Eligible, with occasional work at an Airbnb office or attendance at offsites as agreed with the manager. Candidates must live in a state where Airbnb, Inc. has a registered entity.

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.

Responsibilities

Design rigorous experiments & quasi-experiments to measure the causal impact of CS product launches and drive data-informed launch decisions.
Build causal ML models to optimize Make Goods budget allocation and maximize business impact.
Conduct causal inference analyses to quantify the long-term effects of product changes and uncover heterogeneous treatment effects.
Deliver strategic insights on quality-cost tradeoffs, empowering leadership to deliver the best possible support experience to our community.
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.