Tech Stack

Description

You will work with technical and business teams to develop robust insights, create data narratives, and leverage Airbnb's rich data to define metrics. You'll build advanced analytical capabilities, solve complex problems with data, and collaborate with engineers, product managers, and designers to achieve cross-functional goals. The role focuses on understanding how guests and hosts interact with fraud safety frictions to manage risk while meeting regulatory compliance.

Requirements

• 6+ years of industry experience.
• Advanced degree in Business Analytics or quantitative field (e.g., Statistics, Econometrics, Computer Science, Engineering, Mathematics, Data Science, Operations Research).
• Good communication and collaboration skills.
• Ability to conduct rigorous analysis and communicate to technical and non-technical audiences.
• Proven track record of delivering full stack data projects from inception to business impact.
• Advanced proficiency in SQL/NoSQL, programming languages such as Python, R, SAS.
• Intermediate knowledge of data foundations and databases like Presto, Hive, Teradata, Spark.
• Advanced proficiency in statistical and machine learning techniques, Tableau or similar BI platform.
• Familiarity with EDA, hypothesis testing, significance testing, linear regression, logistic regression, SVM, ridge regression, lasso regression, random forest, basic concepts of NLP, KNN, K-Means, A/B testing, multivariate testing, digital funnel testing.
• Positive and solution-oriented mindset with ability to maintain confidentiality and objectivity.
• Exceptional problem-solving abilities, self-motivation, ability to work autonomously, strong organizational and time management skills.

Responsibilities

• Execute projects with regular direction; set roadmap for projects up to a quarter ahead.
• Ensure data quality and data integrity for analysis and reporting by blending external and internal data.
• Perform data modeling for customers, hosts, cases, agents, etc.
• Execute ad-hoc reporting, data investigations, and root cause analysis into operational areas.
• Devise business metric frameworks and optimization models.
• Influence data-driven decisions across global sites via business reviews, self-serve portal, etc.
• Experiment with new policy/product features to deter and mitigate risk.
• Influence machine learning models in partnership with DS/Engg teams.