Tech Stack

Description

You will lead data-driven roadmaps for safety working groups at Airbnb, building insights and reporting platforms to improve community and agent experiences. Leveraging Airbnb's unique data and machine learning infrastructure, you will define key metrics, drive experimentation, and influence product decisions to keep the platform safe and trusted.

Requirements

• Minimum of 10+ years of industry experience in business analytics.
• Degree (Masters or PhD is a plus) in a quantitative field (e.g., Statistics, Econometrics, Computer Science, Engineering, Mathematics, Data Science, Operations Research).
• Experience supporting safety, risk, Trust Safety, compliance or employee wellbeing in high-volume call center or customer operations environments.
• Expert skills in SQL and expert in at least one programming language for data analysis (Python or R).
• Experience with non-experimental causal inference methods, experimentation and machine learning techniques.
• Working knowledge of schema design and high-dimensional data modeling (ETL framework like Airflow).
• Ability to work under conditions of ambiguity in a fast-growth, sometimes uncertain and complex environment.
• Proven track record of influencing senior leaders and driving outcomes.

Responsibilities

• Lead and drive data-driven roadmaps for the CS Safety working groups.
• Recommend actionable solutions backed by data and metrics to product and operational problems.
• Build and own an insights and reporting platform that measures and improves effectiveness across CS Safety platform and contact center network.
• Perform data modeling of various entities using tools frameworks for optimizing community and agent experiences.
• Define and evaluate key metrics in an unstructured problem space including measurement of ML models.
• Anticipate emerging safety risks through early-warning indicators, trend analysis, predictive modeling, and scenario planning.
• Influence data-driven decisions across business verticals via business reviews, scorecards, self-serve portal, OKRs, and planning.
• Influence experimentation measurement strategies; conduct power analyses, define exit criteria, and use statistical models to improve inference.