Resumen del rol

Marketing Data Scientist

Requisitos y responsabilidades

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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.

Bonus/Nice to Have:

  • 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.
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FocoOperational Data AnalyticsÁrea del rol
Señal de seniorityNivel abiertoNivel del candidato
StackCI/CD, Python, SnowflakeSkills principales
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