Data Scientist (Analytics) - L24

Key Responsibilities

• Own product analytics for key areas of the eBay Live buyer journey , including discovery, viewing, engagement, bidding, purchasing, and retention.
• Partner with Product and Engineering teams to define success metrics, KPIs, and guardrails for new features and experiences.
• Design, analyze, and interpret A/B tests and controlled experiments , ensuring statistical rigor and clear decision criteria.
• Apply advanced measurement approaches, including causal inference and quasi-experimental methods , when traditional A/B testing is not feasible.
• Conduct deep-dive product analyses using funnels, cohorts, segmentation, behavioral analysis, and other statistical techniques .
• Identify friction points and growth opportunities across the buyer journey and translate findings into actionable product recommendations.
• Build scalable dashboards, measurement frameworks, and self-service analytics that enable faster product decision-making.
• Partner with engineering teams to improve instrumentation, event tracking, and data quality .
• Communicate analytical findings clearly to Product, Engineering, Design, and business stakeholders.

Required Qualifications

• 4–8 years of experience in data science, product analytics, or related analytics roles.
• Strong proficiency in SQL and Python .
• Strong hands-on experience with product analytics and A/B testing .
• Solid understanding of statistics, experimental design, hypothesis testing, and causal measurement.
• Experience analyzing large-scale behavioral, transactional, or event-level data .
• Strong analytical problem-solving skills and ability to structure ambiguous questions.
• Strong communication skills and ability to translate complex analysis into clear product recommendations.
• Experience working effectively with cross-functional Product and Engineering teams.

Preferred Qualifications

• Experience with e-commerce, marketplaces, consumer technology, or real-time products .
• Experience with causal inference or advanced experimentation methodologies.
• Exposure to machine learning, segmentation, personalization, or recommendation use cases.
• Experience working in an early-stage or rapidly scaling product environment.
• Interest in leveraging AI and automation to improve analytics workflows.

Additional Details