Marketing Data Scientist, CircleCI

• 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

Desirable

• 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
• 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