Senior Data Scientist

The Proactive Insights team is a newly formed team at the center of Mixpanel's AI-first analytics vision. With a greenfield charter, we're building the intelligent layer that transforms Mixpanel from a tool you query into a partner that works for you. We answer the question every data-driven team asks: 'What changed, why, and what should I do about it?' We proactively keep users informed about what matters in their data, delivering the right insights and recommendations at the right time, to the right places, both inside and outside of Mixpanel.

As the first Data Scientist embedded in product engineering, you'll champion integrating cutting-edge data science techniques into Mixpanel's products and serve as a methodological resource for cross-functional teams tackling problems that benefit from deeper DS expertise. You'll design and validate causal inference approaches that go beyond surface-level correlation, and partner on how those outputs get translated — often via LLMs — into clear, natural-language, actionable experiences for Mixpanel's customers. You'll partner closely with strong product engineers who own the implementation — your job is to make sure the methodology is rigorous, well-documented, and grounded in real outcomes. This is a high-impact, high-autonomy role on a small, fast-moving team.

• 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 move beyond correlation and establish which user behaviors genuinely drive downstream business outcomes.
• Build and own time-series forecasting models that project KPI trajectories against goals — extending our existing use of TimesFM into customer-facing forecasting features.
• Build survival analysis and retention models that underpin Signals and Simulation outputs.
• Develop clustering and behavioral similarity approaches for Cohort Detection that are both statistically sound and interpretable to end users.
• Document methodology clearly — including assumptions, validation approaches, and expected output behavior — so engineers can implement reliably without ambiguity.
• Review and validate that production results match expected statistical behavior, partnering with engineers on edge cases and anomalies.
• Establish rigor around statistical significance, multiple testing correction, and uncertainty quantification so customers can trust what they see.
• Work cross-functionally with internal stakeholders, including Finance and Data Science, to ensure analytical outputs are grounded in real business outcomes.
• Communicate findings and methodology clearly to Product and Engineering — translating statistical concepts into plain language.

• 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 — propensity score matching, regression discontinuity, difference-in-differences, or instrumental variables — with the judgment to choose the right method for a given problem.
• Experience with survival analysis or retention modeling (e.g. Cox proportional hazards, Kaplan-Meier).
• Strong Python fluency across the analytical stack — statsmodels, scikit-learn, pandas, and equivalent libraries for survival analysis, clustering, and time-series modeling.
• Experience with time-series forecasting methods — classical approaches (ARIMA, exponential smoothing) and/or modern foundation models such as TimesFM, Chronos, or similar.
• Experience with clustering and similarity methods applied to behavioral or user data.
• Strong statistical communication — you can explain a propensity score or a survival curve to a PM without losing them.
• SQL fluency for data access, exploration, and validation.
• Comfort working in a product environment where analytical rigor and practical delivery go hand in hand.

• Experience working with large-scale behavioral event data (product analytics, growth, or similar domains).
• Familiarity with feature engineering from raw event streams.
• Experience with structural equation modeling or causal DAGs for multi-metric impact modeling.
• Familiarity with how offline batch analyses are productionized.
• 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 (Claude Code, Cursor, etc.) to accelerate modeling iteration.