# Senior Data Scientist, AI Product Insights

**Company:** [Mixpanel](https://hotfix.jobs/companies/mixpanel)
**Location:** San Francisco, CA
**Role:** Data Science
**Salary:** $226k – $266k/yr
**Experience:** 5+ years
**Skills:** Python, SQL, Causal Inference, survival analysis, time-series forecasting, arima, timesfm, scikit-learn, pandas, statsmodels, clustering, kaplan-meier, regression discontinuity, difference-in-differences, instrumental variables
**Posted:** 2026-08-10

> Develops rigorous statistical and causal models powering Mixpanel’s AI-driven product insights, including forecasting, simulation, retention, and cohort detection. Requires 5+ years of applied statistical modeling experience, strong Python and SQL skills, and expertise in causal inference.

## Job Description

## 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.

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