Senior Data Scientist, AI Product Insights

Mixpanel•San Francisco, CA

2d•$226,000 - $266,000

About The Position

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.

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 — 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

Nice To Haves

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 (directly applicable to Simulation)
Familiarity with how offline batch analyses are productionized, even if you're not implementing them yourself
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 (e.g. hallucination or consistency checks on AI-generated insights)
Comfort using AI coding tools (Claude Code, Cursor, etc.) to accelerate modeling iteration

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 — 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