Job description

About Mixpanel Mixpanel is the leading product intelligence and analytics platform, trusted by more than 29,000 companies to help understand how people use the products they build. By combining powerful analytics with AI that knows your business, Mixpanel helps teams see what’s working, diagnose what’s not, and decide what to build next. Learn more at mixpanel.com . About the Team 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. Some examples of what we are building: Signals : Statistical analysis that automatically identifies which user behaviors cause downstream business outcomes — such as which actions genuinely improve 30-day retention — using causal inference to move beyond correlation Forecasting : Time-series modeling that projects whether a KPI (e.g. Signups) will hit its goal by end of quarter — including trend decomposition, seasonality adjustment, and confidence bands against a target. Simulation : Causal impact modeling that estimates how moving one metric (e.g. weekly sharing rate) by a given amount will ripple through to downstream KPIs like retention or revenue — giving teams a quantified basis for prioritization Cohort Detection : Automated identification of at-risk user cohorts by finding active users who resemble known churned segments across both behavioral patterns and descriptive characteristics, before they churn. Offline batch survival analysis models that estimate each user's probability of a future outcome (e.g. likelihood to churn or convert within 30 days). About the Role 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. 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 We're Looking For Someone Who Has 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 Bonus Points For 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 Compensation The amount listed below is the total target cash compensation (TTCC) and includes base compensation and variable compensation in the form of either a company bonus or commissions. Variable compensation type is determined by your role and level. In addition to the cash compensation provided, this position is also eligible for equity consideration and other benefits including medical, vision, and dental insurance coverage. You can view our benefits offerings here . Our salary ranges are determined by role and level and are benchmarked to the SF Bay Area Technology data cut released by Radford, a global compensation database. The range displayed represents the minimum and maximum TTCC for new hire salaries for the position across all of our US locations. To stay on top of market conditions, we refresh our salary ranges twice a year so these ranges may change in the future. Within the range, individual pay is determined by experience, job-related skills, qualifications, and other factors. If you have questions about the specific range, your recruiter can share this information. Mixpanel Compensation Range $216,000 — $254,000 USD Benefits and Perks Comprehensive Medical, Vision, and Dental Care Mental Wellness Benefit Generous Vacation Policy & Additional Company Holidays Enhanced Parental Leave Volunteer Time Off Additional US Benefits: Pre-Tax Benefits including 401(K), Wellness Benefit, Holiday Break *please note that benefits and perks for contract positions will vary* Culture Values Make Bold Bets: We choose courageous action over comfortable progress. Innovate with Insight: We tackle decisions with rigor and judgment - combining data, experience and collective wisdom to drive powerful outcomes. One Team: We collaborate across boundaries to achieve far greater impact than any of us could accomplish alone. Candor with Connection: We build meaningful relationships that enable honest feedback and direct conversations. Champion the Customer: We seek to deeply understand our customers’ needs, ensuring their success is our north star. Powerful Simplicity: We find elegant solutions to complex problems, making sophisticated things accessible. Why choose Mixpanel? We’re a leader in analytics with over 29,000 customers and $277M invested from prominent investors: like Andreessen-Horowitz, Sequoia, YC, and, most recently, Bain Capital. Mixpanel’s pioneering event-based data analytics platform offers a powerful yet simple solution for companies to understand user behaviors and easily track overarching company success metrics. Our accomplished teams continuously facilitate our expansion by tackling the ever-evolving challenges tied to scaling, reliability, design, and service. Choosing to work at Mixpanel means you’ll be helping the world’s most innovative companies learn from their data so they can make better decisions. Mixpanel is an equal opportunity employer supporting workforce diversity. At Mixpanel, we are focused on things that really matter—our people, our customers, our partners—out of a recognition that those relationships are the most valuable assets we have. We actively encourage women, people with disabilities, veterans, underrepresented minorities, and LGBTQ+ people to apply. We do not discriminate on the basis of race, religion, color, national origin, gender, gender identity or expression, sexual orientation, age, marital status, veteran status, or disability status. Pursuant to the San Francisco Fair Chance Ordinance or other similar laws that may be applicable, we will consider for employment qualified applicants with arrest and conviction records. We’ve immersed ourselves in our Culture and Values as our guiding principles for the impact we want to have and the future we are building.

Requirements

Must-haves

Forecasting : Time-series modeling that projects whether a KPI (e.g. Signups) will hit its goal by end of quarter — including trend decomposition, seasonality adjustment, and confidence bands against a target.
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
We're Looking For Someone Who Has
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

Bonus Points For

Application prep

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Keywords to mirror

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Platform: greenhouse
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)

Parental Leave

Wellness