Data Scientist

Responsibilities

• Design and execute causal analyses to determine the impact of behavior changes, treatments, campaigns, and product experiences on KPIs
• Partner with analytics, product, and data stakeholders to shape experiment design and measurement plans
• Support A/B test analysis, quasi-experimental analysis, and post-readouts with business recommendations
• Identify key drivers of KPI movement and translate findings into actions for lifecycle strategy and targeting
• Define robust measurement frameworks for lead indicators and customer journeys
• Improve analysis confidence through validation logic, data definitions, and measurement consistency
• Contribute to the evolution of data products, semantic models, and analytical workflows
• Communicate findings clearly to business leaders, focusing on implications and tradeoffs

Requirements

• Strong grounding in statistics, experimental design, and causal inference
• Experience analyzing A/B tests or treatment/intervention programs in a business setting
• Ability to work with complex behavioral and customer lifecycle data
• Strong SQL skills
• Practical experience using Python or similar tools for analysis
• Ability to evaluate data quality and identify risks to valid inference
• Comfort translating technical findings into business language
• Good judgment on selecting descriptive, predictive, or causal approaches

Preferred Qualifications

• Experience in subscription, lifecycle, CRM, growth, retention, or customer strategy analytics
• Experience with BigQuery or modern cloud analytics environments
• Familiarity with contribution analysis, driver analysis, uplift thinking, or heterogeneous treatment effects
• Exposure to building reusable analytics assets or internal data science tooling