Responsibilities

• Frame ambiguous marketing problems as well-scoped modeling and measurement challenges and own them end to end.
• Develop and own attribution models that accurately allocate marketing investment across channels and touchpoints.
• Build propensity, lead scoring, and churn models to prioritize where GTM teams should focus.
• Model the relationship between product engagement signals and downstream commercial outcomes — expansion, retention, and conversion.
• Design, execute, and interpret experiments (A/B, MVT, geo-based) with appropriate power analysis and statistical validity.
• Build and apply segmentation models and cohort analyses to uncover behavioral patterns, lifecycle trends, and funnel opportunities.
• Analyze organic search and AEO signals as modeling inputs to inform content strategy and improve discoverability.
• Partner with analytics engineers to productionize models and move insights from notebook to pipeline.
• Use AI tools to move faster, explore unfamiliar methods, and surface modeling options — while developing the judgment to know when AI-generated outputs are wrong or incomplete.

Requirements

• Bachelor's degree in Statistics, Mathematics, Computer Science, Data Science, Economics, or a related quantitative field; Master's or PhD is a bonus.
• 4 to 6 years applying data science in a product, growth, or marketing context at a high-growth company.
• Strong command of Python (pandas, scikit-learn, statsmodels, or similar) and analytical SQL.
• Demonstrated experience building predictive models that influenced real business decisions.
• Hands-on experience with marketing measurement — attribution, media mix modeling, incrementality testing, or similar.
• Solid statistical foundation — forecasting, regression, classification, causal inference, and experiment design.
• Experience with GA4, Google Ads, and digital marketing measurement platforms.
• Strong understanding of reverse ETL processes and operationalizing model outputs.
• Strong storytelling skills — able to translate statistical complexity into clear, actionable business language for both technical and non-technical audiences.

Preferred

• Experience with MLOps practices and moving models from experimentation to production.
• Comfort working across the full stack — dbt, Hex, Snowflake, Looker, Mode, Segment, and similar tools.
• Familiarity with SaaS, developer tools, or B2B product-led growth metrics.
• Hands-on experience with AI tools like Claude, Cursor, or similar LLM-powered assistants to accelerate analytical workflows.

Benefits