Senior Data Scientist - Marketing

DeepL DeepL · AI Frontier · London, United Kingdom · Marketing

Senior Data Scientist role focused on marketing analytics, causal inference, attribution modeling, and predictive modeling for B2B customer acquisition and revenue optimization. Requires expertise in SQL, dbt, Python for statistical modeling, and cloud data platforms. The role aims to build data pipelines, develop measurement frameworks, and inform strategic planning to optimize marketing spend and sales alignment.

What you'd actually do

  1. Drive an evidence-based culture by translating complex causal models into actionable playbooks and self-service dashboards, aligning diverse stakeholders and priorities to ensure trust in measurement and clarity in strategic decision-making.
  2. Take ownership of the end-to-end data lifecycle by writing production-grade dbt models and building a scalable data architecture for the marketing domain. This includes integrating fragmented data from CRM systems (e.g., Salesforce), web analytics, and marketing platforms, while applying rigorous data quality tests and maintaining the integrity of our data pipelines.
  3. Design and execute causal inference methods involving A/B, incrementality and holdout tests to isolate the true incremental impact of marketing spend.
  4. Build models to predict lead quality, Customer Lifetime Value (LTV) and revenue potential, helping the business distinguish between high-volume traffic and high-value conversion paths.
  5. Develop forecasting tools and Marketing Mix Models (MMM) to inform long-range strategic planning and optimise our channel mix for both B2C and B2B revenue.
  6. Develop and maintain attribution models (MTA, DDA) that account for long consideration cycles and multiple touchpoints, whether at an individual or account level.

Skills

Required

  • SQL
  • dbt
  • Python
  • CausalML
  • DoWhy
  • Statsmodels
  • LightweightMMM
  • GoogleMeridian
  • DataBricks
  • Snowflake
  • BigQuery
  • A/B testing
  • Incrementality testing
  • Holdout testing
  • Marketing Mix Models (MMM)
  • Multi-Touch Attribution (MTA)
  • Data-Driven Attribution (DDA)
  • Customer Lifetime Value (LTV) modeling
  • Lead quality prediction
  • Forecasting
  • Data pipeline development
  • Data quality testing
  • Statistical modeling
  • Econometrics

Nice to have

  • B2B experience
  • SaaS experience
  • High-consideration B2C experience

What the JD emphasized

  • 5–8+ years of experience in Marketing Science, Data Science, or Econometrics
  • Expert in SQL and data transformation tools (e.g., dbt)
  • Strong Python skills for statistical modelling
  • Hands-on experience measuring incremental impact of marketing and sales initiatives using a variety of methods
  • Proven ability to model complex, multi-touch journeys and the relationship between marketing activity and sales-assisted outcomes.