Job Description

THE ROLE & THE TEAM

The Marketing Science team acts as the engine behind Zalando’s growth, partnering with business units across the full marketing funnel. We extend the Zalando experience beyond our platform, connecting customers with the fashion inspiration and shopping convenience they love. By leveraging cutting-edge technology, we ensure our marketing is relevant, engaging, and deeply respectful of customer privacy.

We collaborate closely with product managers, engineers, and analysts to refine our industry-leading marketing engine. Our mandate covers:

• Rigorous Experimentation Establishing the "ground truth" framework for measuring marketing effectiveness.

• Advanced Modeling Building attribution models to value touchpoints and propensity models to refine targeting.

• Audience Optimization creating customer uplift models to identify incremental value and building audiences that improve marketing efficiency.

• Investment Strategy Steering external advertising platforms and optimizing marketing spend to align with core business objectives.

As a Senior Applied Scientist, you will own the Brand Measurement scientific roadmap, working with Principal Scientists and leaders to maximize marketing efficiency by moving beyond simple proxy metrics to true causal incrementality.

• Measure Design and implement advanced, non-linearly scaled experimentation frameworks (such as quasi-experiments and synthetic controls) to measure the causal impact of online and offline brand marketing investments where traditional RCTs are unavailable.

• Optimize Develop, calibrate, and scale sophisticated Marketing Mix Models (MMM) to provide strategic investment recommendations that optimize toward long-term business goals.

• Steer Directly influence how Zalando allocates marketing investment, ensuring we mathematically attribute value correctly across the entire funnel and optimize for long-term equity growth.

INCLUSIVE BY DESIGN

If you think you have what it takes, we encourage you to apply even if you don't meet every single requirement. You may just be the right candidate for this or other roles!

At Zalando, our vision is to be the leading pan-European ecosystem for fashion and lifestyle e-commerce – one that thrives on diversity and is truly inclusive by design. We believe that diverse teams fuel innovation and creativity, and we actively seek out talent from all backgrounds.

We actively seek to reduce bias in our hiring and employment processes, focusing on your qualifications, skills, and contributions. To support this, we kindly ask that you refrain from including personal details such as your photo, age, or marital status in your CV, ensuring a fair and equitable evaluation based solely on your abilities and potential.

We are committed to providing an exceptional and accessible candidate experience for everyone. If you require any accommodations to support you throughout the hiring process, please let us know – we are here to assist you.

Discover more about our commitment to creating a diverse and inclusive workplace:https://jobs.zalando.com/en/our-culture/diversity-and-inclusion

WHAT WE’D LOVE YOU TO DO (AND LOVE DOING)

• Take ownership of highly ambiguous, macro-level research questions, prioritizing pragmatic, rapid solutions to establish causal baselines before iterating for algorithmic complexity.

• Apply advanced Deep Learning, Bayesian time-series, and CausalML methodologies to bridge offsite brand awareness to long-term value add.

• Champion MLOps best practices by ensuring models are production-ready, thoroughly tested via automated unit/integration tests, and robustly documented.

• Mentor junior and mid-scientists, conduct rigorous code/methodology reviews, and foster a culture of continuous learning and scientific excellence within the team.

• Bridge the gap between deeply technical methodologies and commercial strategy, confidently defending scientific integrity to executive stakeholders and translating model parameters into actionable business decisions.

WE’D LOVE TO MEET YOU IF

• A strong academic foundation (Master's or PhD preferred) in Econometrics, Statistics, Machine Learning, or Causal Inference, paired with at least 5 years of practical industry experience specifically within marketing science or measurement.

• Proven experience building, deploying, and maintaining Marketing Mix Models (MMM) and a deep theoretical understanding of adstock transformations, saturation curves, and Bayesian priors.

• Deep proficiency in Python, SQL, and PySpark, with a commitment to high coding standards, including active participation in code reviews and elevating the team's engineering culture.

• A self-starter with a proven track record of architecting measurement frameworks in high-ambiguity environments where user-level tracking is limited (e.g., ATT, cookieless environments).

• Demonstrated experience guiding, upskilling, and mentoring mid-to-junior data scientists and driving the team's technical roadmap.

• Exceptional communication and negotiation abilities, capable of aligning incentives between Performance Marketing, Brand Marketing, and Finance leaders while maintaining absolute technical precision.

OUR OFFER

Zalando provides a range of benefits, here’s an overview of what you can expect. Ask your Talent Acquisition Partner to learn more about what we offer.

• 27 days of holiday a year to start for full-time employees (+1 day for every calendar year up to 30 days)

• 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

• Employee shares program

• 40% off fashion and beauty products sold and shipped by Zalando, 30% off Lounge by Zalando, discounts from external partners

• Relocation assistance available (subject to prior agreement)

• Family services, including counseling and support

• Health and wellbeing options (including Wellhub, formerly Gympass)

• Mental health support and coaching available

• Drive your development through our training platform and biannual peer-to-peer review