About the role
About
the Role: The Marketing Data Scientist is a rigorous, intellectually curious problem solver embedded directly within the Marketing function for the GTM team. They own the science behind how we understand, attract, and grow our customers — building the models, measurement frameworks, and experiments that turn complex marketing data into decisions that move the business forward. They do not just report on what happened — they design the studies that tell us why and build the models that tell us what comes next.
What You'll Do: 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. What You'll Bring: 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.
