Senior Data Scientist, Mixpanel

• 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

Desirable

• 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 — such as adaptive experimentation.
• You won't just advise on Proactive Insights; you'll be the analytical brain driving how Signals, Forecasting, Simulation, Predictions, and Cohort Detection actually work
• Your models are the reasoning layer behind an AI system that proactively tells customers what changed, why, and what to do next — and increasingly, the layer behind an agent that acts on their behalf.
• As more of this experience becomes agentic, rigorous causal grounding is what separates a trustworthy recommendation from a plausible-sounding one. You'll be the person who makes sure it's the former
• 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 own the rigor, the system owns the explanation.
• 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. You'll also collaborate cross-functionally with teams like AI platform, analysis, and data infrastructure to scale your analytic solutions beyond what you could build alone
• This is a high-impact, high-autonomy role on a small, fast-moving team. You'll have significant influence over the analytical direction of a new product category at Mixpanel that helps thousands of companies understand what truly drives their most important metrics
• 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 — a model is only as trustworthy as the data feeding it
• 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