Staff Analyst, Advanced Analytics, Payments

Airbnb Airbnb · Consumer · United States · Analytics

Staff Analyst role focused on Advanced Analytics for Airbnb's global Payments operations. The role involves driving data-informed decision-making, optimizing user journeys, and solving complex domain challenges using quantitative measurement, machine learning models, and AI. Key responsibilities include architecting reporting platforms, developing analytical frameworks, and influencing strategy through data and experimentation, with a focus on payments processing, risk, and compliance.

What you'd actually do

  1. Act as a vital data thought partner to leadership, delivering actionable insights and recommendations that empower integrated, data-informed decision-making across the organization.
  2. Champion day-to-day analytics while architecting scalable reporting platforms to enhance the efficacy of our payments ecosystem.
  3. Pinpoint frictions in the user journey to optimize experiences for our global Guest, Host, and agent communities through rigorous problem-solving.
  4. Solve complex domain challenges using advanced quantitative measurement, machine learning models, AI, and experimentation to provide fresh perspectives on existing solutions.
  5. Develop sophisticated analytical frameworks and statistical models to establish causal inference and drive measurable business impact.

Skills

Required

  • 10+ years of industry experience in business analytics
  • Masters or PhD in a quantitative field (e.g., Statistics, Econometrics, Computer Science, Engineering, Mathematics, Data Science, Operations Research)
  • Proven track record of working in Payments organizations
  • Deep technical proficiency across payments domain areas, such as payment processing, risk, and compliance
  • Expert skills in SQL
  • Expert in at least one programming language for data analysis (Python or R)
  • Strong storytelling skills
  • Experience in using AI tools to scale workflows, and identify new areas of opportunity
  • 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)

What the JD emphasized

  • Payments organizations
  • payment processing
  • risk
  • compliance
  • machine learning models
  • AI

Other signals

  • AI/ML models
  • quantitative measurement
  • advanced analytical frameworks
  • statistical models
  • causal inference
  • experimentation
  • machine learning tools