Senior Data Scientist, Trust (inference)

Airbnb Airbnb · Consumer · United States · Data Science

Senior Data Scientist role focused on Trust at Airbnb, applying ML and causal inference to detect and defend against adversarial behavior like fraudulent listings, fake inventory, review manipulation, and account takeovers. The role involves designing measurement and evaluation frameworks, leading experiments, building statistical and Bayesian models for risk quantification and treatment effects, and generating insights to inform product and policy decisions. Emphasis on rigorous statistical thinking, applied ML, and influencing cross-functional teams to enhance platform safety and user trust.

What you'd actually do

  1. Inference & Measurement: Design and implement measurement and evaluation frameworks to understand the impact of Trust defense, and uncover opportunities for improvement
  2. Experimentation: Collaborating closely with cross-functional partners, lead the design and analysis of experiments and quasi-experiments in environments where standard A/B testing is challenging.
  3. Modeling: Build and iterate on statistical and Bayesian models that quantify risk, estimate treatment effects, and surface measurement gaps; provide causal interpretation of signals surfaced by ML systems
  4. Insights and Strategy: Generate deep insights on the effectiveness of Trust defenses and translate them into clear strategic recommendations for product roadmap and policy decisions
  5. Communication & Collaboration: Deliver robust, leadership-ready research with rigorous analysis, compelling data visualizations, and clear narratives; effectively communicate complex methodological tradeoffs to both technical and non-technical audiences across product, engineering, policy and operations

Skills

Required

  • 5+ years of industry experience in a quantitative analysis role with a Master's degree in a quantitative field (statistics, economics, computer science, etc.), or 2+ years of experience with PhD in relevant fields.
  • Deep expertise in experimentation and causal inference.
  • Strong knowledge of Bayesian modeling and its application to measurement, uncertainty quantification, and decision-making
  • Proven ability to design and lead experimentation programs in complex environments, collaborating with and influencing cross-functional stakeholders.
  • Skilled in statistical programming and database usage.
  • Strong coding skills in SQL and python or R.
  • Demonstrated ability to translate complex methodological findings into actionable insights and compelling narratives for audiences of varying technical levels
  • Excellent judgment in navigating ambiguity, scoping problems, and driving impact through influence across cross-functional teams

What the JD emphasized

  • rigorous statistical thinking
  • applied ML
  • quantify risk
  • estimate treatment effects
  • measurement gaps
  • causal interpretation
  • effectiveness of Trust defenses
  • strategic recommendations
  • complex methodological tradeoffs

Other signals

  • fraud detection
  • risk modeling
  • adversarial behavior detection
  • trust and safety