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Applied Scientist - Economics & Experimentation

Applied Scientist - Economics & Experimentation

As an Applied Scientist working in Economics & Experimentation, you are improving and extending the automated analysis capabilities in our AB-Testing platform. You increase the accuracy and usefulness of A/B tests for our business stakeholders with advanced statistical, causal inference, and machine learning methodologies that enable Zalando teams to measure their impact on customers. You work closely with experienced backend- and frontend engineers, and product managers to turn research and product opportunities into scalable and performant productive systems (e.g. the A/B testing platform handles 10s of thousands of requests per second and petabytes of data).


  • Bring in your scientific experience in researching and applying causal inference and machine learning based solutions to help our millions of customers have a great experience on Zalando.
  • Autonomously or with other applied scientists, develop, evaluate, and deliver applied science projects from inception through to production in big data systems.
  • Help maintain and improve production ML pipelines as well as other research software and services
  • Proactively contribute towards research roadmaps by identifying blind spots or proposing pragmatic alternative solutions.


  • PhD or MSc (or comparative experience) in Statistics, Computer Science, Mathematics, or Physics.
  • An understanding of state of the art causal inference and machine learning techniques and how they can be applied to large scale e-commerce problems.
  • Solid industry experience in Big Data stream- and batch processing (e.g. Spark and AWS).
  • Great coding skills in Python and SQL
  • Experience with or strong desire to learn JVM (especially Scala) is a big plus
  • Solid software development skill set (e.g. test driven development, strong documentation)
  • Hunger to quickly learn new methods, tools, and processes that can bring value to our systems and users
  • Team player with strong driver capabilities.
  • Excellent verbal and written communication skills with a detail oriented and self-starter mindset.


  • Culture of trust, empowerment and constructive feedback, open sourcecommitment, meetups, game nights, 70+ internal technical and fun guilds, knowledge sharing through tech talks, internal tech academy and blogs, product demos, parties & events
  • Competitive salary, employee share shop, 40% Zalando shopping discount, discounts from external partners, centrally located offices, public transport discounts, municipality services, great IT equipment, flexible working times, additional holidays and volunteering time off, free beverages and fruits, diverse sports and health offerings
  • Extensive onboarding, mentoring and personal development opportunities and an international team of experts
  • Relocation assistance for internationals, PME family service and parent & child rooms* (*available in select locations)

We celebrate diversity and are committed to building teams that represent a variety of backgrounds, perspectives and skills. All employment is decided on the basis of qualifications, merit and business need.

ABOUT Economics & Experimentation:

The department enables Zalando to make robust data-driven decisions. We research the new trusted statistical and machine learning methods that teams across the company will use to measure their customer impact. We scale the methods to the company by building our custom A/B testing platform and educating stakeholders how best to incorporate experimentation and causal inference into their product development processes. To achieve our mission, we bring together diverse skill sets combining econometrics, machine learning, and software engineering

Zalando SE

Zalando is Europe’s leading online platform for fashion and lifestyle, connecting customers, brands and partners across 17 markets. We drive digital solutions for fashion, logistics, advertising and research, bringing head-to-toe fashion to more than 35+ million active customers through diverse skill-sets, interests and languages our teams choose to use.

Please note that all the applications must be completed using the online form - we do not accept applications via e-mail.

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