Senior Data Scientist (AI Product Insights)

TL;DR

Senior Data Scientist, AI Product Insights (Causal Inference/Forecasting): Building the analytical foundation for proactive product insights, including causal signals, KPI forecasts, simulations, predictions, and cohort detection, with an accent on statistical rigor, interpretability, and trustworthy AI-driven recommendations. Focus on designing causal inference methods, productionizing time-series and survival models, and validating how analytical outputs are translated into actionable customer experiences.

Location: San Francisco, US; hybrid work required

Salary: $226,000–$266,000 USD total target cash compensation

Company

hirify.global is a product intelligence and analytics platform that helps companies understand user behavior and make data-informed product decisions.

What you will do

• Own the analytical design, methodology selection, validation, and iteration for Signals, Forecasting, Simulation, Predictions, and Cohort Detection.
• Design causal inference approaches that identify behaviors driving downstream business outcomes beyond simple correlation.
• Build time-series forecasting, survival analysis, retention, clustering, and behavioral similarity models for customer-facing insights.
• Assess data quality and trust prerequisites before extending predictive features to customers.
• Document assumptions, validation methods, uncertainty, and expected behavior so engineers can implement models reliably.
• Partner with Product, Engineering, AI Platform, Data Infrastructure, Finance, and Data Science teams to validate production behavior and communicate findings clearly.

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 experience with causal inference methods such as propensity score matching, regression discontinuity, difference-in-differences, or instrumental variables.
• Experience with survival or retention modeling, time-series forecasting, clustering, and similarity methods applied to user data.
• Strong Python skills with tools such as statsmodels, scikit-learn, pandas, and equivalent modeling libraries, plus SQL fluency.
• Ability to explain statistical methods clearly and collaborate in a product engineering environment.

Nice to have

• Experience with large-scale behavioral event data, product analytics, growth platforms, or analytics and observability products.
• Experience with TimesFM, Chronos, structural equation modeling, causal DAGs, or feature engineering from raw event streams.
• Experience productionizing offline analyses or working directly in a production codebase alongside engineers.
• Experience evaluating LLM-generated explanations and recommendations against statistical outputs.
• Comfort using AI coding tools such as Claude Code or Cursor.

Culture & Benefits

• Hybrid work environment with a high-impact, high-autonomy role on a small, fast-moving team.
• Medical, vision, and dental insurance coverage.
• Mental wellness benefit and generous vacation policy with additional company holidays.
• Enhanced parental leave and volunteer time off.
• US benefits including pre-tax benefits, 401(k), wellness support, and a holiday break.
• Culture centered on rigorous insight, bold decisions, cross-functional collaboration, candor, customer focus, and simplicity.