Senior Applied Scientist (All Genders) Marketing Science
Company: Zalando
Location: Berlin, Berlin, Germany (Hybrid: Up to 60% remote per week)
Employment Type: Full-time
Seniority Level: Senior level
Estimated Salary: €85,000 - €110,000 per year + Employee Shares (Based on current tech ecosystem benchmarks for senior-tier machine learning and econometrics specialists in Berlin)
Industry: Internet / E-Commerce / Technology
Company Overview
Zalando is Europe’s top pan-European ecosystem for fashion and lifestyle e-commerce, connecting millions of active customers with leading global brands.
At the core of our growth strategy, the Marketing Science team drives high-impact engineering and research, powering our full-funnel expansion.
We build tech that keeps our external advertising sharply targeted, privacy-safe, and mathematically optimized. We work from an inclusive foundation, valuing different perspectives, open workflows, and strong scientific integrity to shape the future of digital retail.
Key Responsibilities
Causal Inference Infrastructure: You’ll lead the scientific roadmap for Brand Measurement, designing advanced, non-linear experimentation frameworks, quasi-experiments, and synthetic controls when RCTs aren’t possible.
Advanced Model Engineering: Create, tweak, and scale sophisticated Bayesian time-series and Deep Learning pipelines, designing next-generation Marketing Mix Models (MMM) to track adstock shifts and saturation effects.
Strategic Investment Steering: Turn broad and ambiguous marketing questions into specific algorithmic models, advising executives on how to best allocate cross-funnel marketing spend to secure long-term business equity.
Production-Grade MLOps: Set engineering standards on the data science team by delivering production-ready code with automated unit tests, integration checks, and clear technical documentation.
Technical Mentorship: Raise the bar for your team by running tough code and method reviews, mentoring junior and mid-level applied scientists, and helping guide the technical roadmap.
Experience & Qualifications
You have 5+ years of hands-on industry experience in marketing science, causal inference, or high-dimensional measurement systems.
You’ve built measurement networks in cookieless or restricted data setups (like navigating Apple’s ATT framework).
You have deep theoretical knowledge in econometrics, Bayesian priors, saturation curves, and propensity score matching.
You’re a clear communicator who can confidently present technical models and back up scientific decisions to Brand, Performance Marketing, and Finance execs.
Education: You hold a Master’s degree or PhD in Econometrics, Statistics, Machine Learning, Computer Science, or another highly quantitative field.
Technical Skills Required
Core Programming & Data Pipelines: You know Python, SQL, and distributed data processing with PySpark inside and out.
Machine Learning & Statistical Frameworks: You’ve got hands-on experience with advanced Python packages for causal machine learning (CausalML), Bayesian modeling (PyMC, Stan, or lightweight MMM libraries), and deep learning architectures.
Software Engineering Practices: You’re comfortable with git workflows, automated testing, CI/CD deployments, and writing clean, well-documented code.
Benefits & Perks
Flexible Balance: Enjoy a hybrid week (up to 60% remote) and the option to work from abroad up to 30 working days a year.
Rest & Volunteerism: Start with 27 days of paid holiday (growing up to 30 based on tenure), plus 2 paid volunteering days each year.
Corporate Equity & Discounts: Get direct access to the employee shares program and an exclusive 40% discount on fashion and beauty products shipped by Zalando.
Relocation Support: Specialized relocation packages available for international hires, pending prior agreement.
Comprehensive Well-being: Access integrated mental health coaching, family counseling, and corporate fitness through Wellhub.
How to Apply
If you’re ready to help shape the future of Marketing Science and turn high-ambiguity research into real, causal business growth, upload your CV and a summary of recent research or model implementations to our Berlin careers portal. For a fair review, please leave out personal photos, age, and marital status from your documents.