Senior Data Scientist, AI Product Insights

Responsibilities

• Own the end-to-end analytical design for Signals, Forecasting, Simulation, and Cohort Detection, including methodology selection, statistical validation, and iteration based on results.
• Assess data quality and trust prerequisites before extending forecasting or predictive features to customers.
• Design and apply causal inference methods to establish which user behaviors drive downstream business outcomes.
• Build and own time-series forecasting models that project KPI trajectories against goals, extending TimesFM into customer-facing forecasting features.
• Build survival analysis and retention models supporting Signals and Simulation outputs.
• Develop statistically sound and interpretable clustering and behavioral similarity approaches for Cohort Detection.
• Document methodologies, assumptions, validation approaches, and expected output behavior for reliable engineering implementation.
• Review production results against expected statistical behavior and partner with engineers on edge cases and anomalies.
• Establish rigor around statistical significance, multiple-testing correction, and uncertainty quantification.
• Work cross-functionally with Finance and Data Science to ground analytical outputs in business outcomes.
• Communicate findings and methodology clearly to Product and Engineering.

Requirements

• MS or PhD in Statistics, Economics, Mathematics, or a related quantitative field, or equivalent industry experience with demonstrated causal inference expertise.
• 5+ years of experience applying statistical modeling to real-world product or business problems.
• Hands-on causal inference experience, including methods such as propensity score matching, regression discontinuity, difference-in-differences, or instrumental variables.
• Experience with survival analysis or retention modeling, such as Cox proportional hazards or Kaplan-Meier.
• Strong Python fluency across the analytical stack, including statsmodels, scikit-learn, pandas, and equivalent libraries for survival analysis, clustering, and time-series modeling.
• Experience with time-series forecasting methods, including classical approaches such as ARIMA and exponential smoothing and/or modern foundation models such as TimesFM or Chronos.
• Experience with clustering and similarity methods applied to behavioral or user data.
• Strong statistical communication skills.
• SQL fluency for data access, exploration, and validation.
• Comfort working in a product environment where analytical rigor and practical delivery go hand in hand.

Nice-to-Haves

• Experience with large-scale behavioral event data, product analytics, or growth.
• Familiarity with feature engineering from raw event streams.
• Experience with structural equation modeling or causal DAGs for multi-metric impact modeling.
• Familiarity with productionizing offline batch analyses.
• Comfort working directly in a production codebase alongside engineers.
• Experience at an analytics, observability, or growth platform.
• Experience evaluating or grounding LLM-generated explanations or recommendations against statistical outputs.
• Comfort using AI coding tools such as Claude Code or Cursor.

Compensation and Benefits

• Total target cash compensation includes base compensation and variable compensation in the form of either a company bonus or commissions.
• Variable compensation type is determined by role and level.
• Eligible for equity consideration and other benefits.