Mixpanel
Senior Data Scientist, AI Product Insights
San Francisco, US (Hybrid) · Senior
Sponsorship not specified$216k-$254kDetected 8 days ago
PythonSQLscikit-learnPandasData ScienceLLMsStatisticsForecastingCommunicationPlain Language
Stay score
odds of building a lasting career here
59Sponsors, lottery-bound
Cap-exempt (no lottery)0
Sponsors this role80
Entry-level history0
PERM / green-card track0
Lottery odds (Level IV)94
Fits your clock70
Sponsors, but it's cap-subject — you still face the weighted lottery (~61% per draw at Level IV). Good if you win; have a cap-exempt backup on your list.
Lottery odds assume a STEM candidate.
Personalize to your clock →H-1B wage level
the lottery is wage-weighted — each level is one more entry
Level I$110,3861 entry
Level II$142,4382 entries
Level III$174,5123 entries
Level IV$206,5654 entries
This range already reaches Level IV — the maximum four lottery entries.
DOL prevailing wage, 2026-27 wage year · Data Scientists (15-2051) · San Francisco-Oakland-Fremont, CA. Wage level is derived by USCIS from the offered wage, occupation and worksite; the occupation shown is inferred from the job title.
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from this employer's Department of Labor filings
Files H-1B transfers
2 transfer filings in the last year, covering 2 workers. Median labor-condition decision: 7 days. An employer that already files transfers is one that can take over an existing H-1B.
Sourced from Department of Labor LCA, PERM and prevailing-wage disclosure data. Employer matching is by name, so figures may be split across an employer's legal entities. Absence of a filing means none appears in our copy of the data, not that none exists.
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About the role
- 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.
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
- 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
- We tackle decisions with rigor and judgment - combining data, experience and collective wisdom to drive powerful outcomes.
- Innovate with Insight: We tackle decisions with rigor and judgment - combining data, experience and collective wisdom to drive powerful outcomes.
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
- Experience with survival analysis or retention modeling (e.g. Cox proportional hazards, Kaplan-Meier)
- 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
- Experience with structural equation modeling or causal DAGs for multi-metric impact modeling (directly applicable to Simulation)
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.
- 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.
- 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.
Benefits
- Comprehensive Medical, Vision, and Dental Care
- Mental Wellness Benefit
- Generous Vacation Policy & Additional Company Holidays
- Enhanced Parental Leave
- Volunteer Time Off
- Pre-Tax Benefits including 401(K), Wellness Benefit, Holiday Break
- please note that benefits and perks for contract positions will vary*
Company info
- 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.
- "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:
- 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.
- 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).
- 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
- Establish rigor around statistical significance, multiple testing correction, and uncertainty quantification so customers can trust what they see
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This listing is sourced directly from Mixpanel's careers page and normalized into a canonical job model.