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The Demand Data & Analytics, Planning, Steering, and Performance Measurement Team focuses on monitoring and analyzing the effectiveness of our online and offline marketing campaigns and broader, on creating a better understanding of our customers' development. Using all available information and building analytics products, we assess the value of each marketing campaign, as well as overall demand evolution and other variables to assess their impact on the overall business and plan a short and long term marketing strategy. As a Senior Applied Scientist, you will drive scientific research progress to find the best approaches for solving complex business problems. You will move beyond task-level execution to independently tackle ambiguous challenges, removing uncertainty in marketing budget steering through a rigorous scientific experimentation framework. Your primary mission is to establish Incrementality Testing as the ground truth for validating and calibrating Media Mix Models.
Job Responsibility
Innovate on MMM Architecture: Advance our modeling by developing custom solutions that account for the unique dynamics of a Flash Sales model, implementing time-varying coefficients and stock availability constraints to ensure high-precision forecasting
Drive the Causal Inference & Incrementality Roadmap: Design and lead a comprehensive research plan for Geo-Lift and Incrementality tests to serve as the definitive 'ground truth' for validating and calibrating our Media Mix Models
Establish Validation Excellence: Act as the gatekeeper of scientific integrity by implementing rigorous Out-of-Sample validation, Bayesian calibration, and Multicollinearity diagnostics before models influence multi-million euro budget decisions
Master Large-Scale Data Environments: Leverage the AWS Ecosystem (S3 & Redshift) as well as Databricks to engineer complex features and process high-velocity datasets, turning raw signals into robust inputs for causal modeling
Bridge Science and Strategy: Take accountability for technical relationships with external MMM partners, aggressively challenging methodologies that conflict with Lounge business reality. Collaborate with main stakeholders like Marketing, Analytics and Engineering as well as external partners to translate complex Causal Inference outputs into actionable investment reallocations for Marketing leadership
Act as an Expert of Marketing Data Science: Keep up to date with the newest trends in the area of marketing, analytics and data science and exchange closely with your peers across the business by providing guidance
Requirements
Master’s degree in a data-driven scientific field such as Statistics, Econometrics, Machine Learning or Mathematics
at least 5 years in a similar analytical context with comparable questions to the area of marketing measurement (MMM, incrementality testing incl. geo-testing & conversion lift studies) with a preference of a Saas/consultancy background
Deep understanding of Causal Inference, Bayesian statistics and experimental design methodologies
Professional experience in Python or R for scientific computing and advanced SQL for data extraction from Redshift
Ability to work backwards from customer/business problems to discover new research opportunities and explain technical results to non-experts
Quick thinking and proactive attitude, able to adapt to change and work independently
Nice to have
Familiarity with Databricks and Tableau
What we offer
Employee shares program
40% off fashion and beauty products sold and shipped by Zalando, 30% off Zalando Lounge, discounts from external partners
2 paid volunteering days a year
Hybrid working model with up to 60% remote per week, actual practice is up to each team to best support their collaboration
Work from abroad for up to 30 working days a year
27 days of vacation a year to start
Relocation assistance available (subject to prior agreement)
Family services, including counseling and support
Health and wellbeing options (including Gympass)
Mental health support and coaching available
Drive your development through our training platform and biannual peer-to-peer review