Marketing Data Scientist
$145,000–$196,000 year
Remote · United States
Job Summary
Marketing Data Scientist role embedded in the Marketing function for the GTM team. You will own the science behind understanding, attracting, and growing customers by building models, measurement frameworks, and experiments that turn marketing data into decisions. Responsibilities include framing marketing problems as modeling challenges, developing attribution models, building propensity/lead scoring and churn models, modeling product engagement against business outcomes, designing and interpreting experiments with power analysis, performing segmentation and cohort analyses, analyzing organic search signals for content strategy, partnering with analytics engineers to productionize models, and leveraging AI tools to accelerate analytics while maintaining judgment on AI outputs. Required skills include advanced degree in a quantitative field (Bachelor's required, Master’s/PhD bonus), 4–6 years of relevant experience, proficiency in Python and SQL, experience with GA4 and digital marketing measurement, strong statistical foundations, and proven ability to translate complex analytics into actionable business insights. Bonus: MLOps, full-stack tooling (dbt, Snowflake, Looker, Mode, Segment), SaaS/B2B metrics familiarity, and experience with AI/LLM-assisted workflows. Base pay range: $145,000–$196,000 USD.
Required Qualifications
- 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
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