DeepL - Senior Data Scientist

• Marketing Partnership & Enablement • 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. • Marketing Data Architecture • 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. • Causal Measurement Strategy • Design and execute causal inference methods involving A/B, incrementality and holdout tests to isolate the true incremental impact of marketing spend. • Predictive Modelling & LTV • 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. • Scenario Planning • 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. • Complex Journey Attribution • Develop and maintain attribution models (MTA, DDA) that account for long consideration cycles and multiple touchpoints, whether at an individual or account level. • QUALITIES WE LOOK FOR • 5–8+ years of experience in Marketing Science, Data Science, or Econometrics. Experience in B2B, SaaS, or high-consideration B2C is highly desirable • Expert in SQL and data transformation tools (e.g., dbt); able to build and maintain robust data pipelines • Strong Python skills for statistical modelling (proficient with libraries such as CausalML, DoWhy, Statsmodels, LightweightMMM, GoogleMeridian) • Comfortable with modern cloud data platforms (e.g., DataBricks, Snowflake, BigQuery) • Hands-on experience measuring incremental impact of marketing and sales initiatives using a variety of methods (e.g., quasi-experimental designs, matching, MMM, and Causal ML). • Proven ability to model complex, multi-touch journeys and the relationship between marketing activity and sales-assisted outcomes. • Excellent communication skills with the ability to tell compelling stories with data and explain complex concepts (like incremental lift) to non-technical partners. • A self-starter with a proven ability to act as a "product owner" for data initiatives, effectively prioritising multiple workstreams and to structure complex, open-ended business problems