Principal Applied Scientist - Zalando Marketing Services

14 Oct 2021
08 Nov 2021
As a Principal Applied Scientist, you will develop and improve real-time recommendation engines for our Sponsored Product and Campaign advertisements.

Every time a customer searches for products on Zalando, a bidding request is sent to our system in real-time. The system aggregates relevant data points, evaluates thousands of candidate ads with our machine learning models, and decides what product to show where - all within 10-30 ms. Every second, we score tens of thousands of ad contents

In this position, you will lead our optimization effort that combines high-load low-latency Engineering, Machine Learning and auction theory. The technical scope covers Large Scale Bayesian Inference, Incrementality Modelling, Deep Learning, Large Scale Online Experimentations, Auction Design and so on. In the future we expect to add Image Processing/Generation and Natural Language Processing to the mix.
  • Improve our real-time recommender system, using data like cross-device graph, user history, browsing history, customer preference, various metadata, images and embeddings of items and contents etc.
  • Improve the pace of innovation and experimentation by improving our approach to optimization
  • Collaborate with brilliant Product Managers, Data Scientists, Engineers and Analysts across Zalando to create positive customer impact together
  • Help define our team's objective. Continuously improve the self-organization of the team.
  • Mentor and grow junior and mid-level members of the team
  • At least one peer reviewed publications in relevant fields like ad optimization, recommendation, information retrieval, machine learning is required (please include it in your application)
  • At least 5 years of experience in optimizing business metrics through automated algorithms (recommendation engines, information retrieval, pricing etc.)
  • Good understanding of the theory behind Machine Learning methods, as well as knowledge of best practices in productionalizing them in a real-time systems
  • Experience with large volumes of sparse categorical data, linear models, Python is a strong plus, but not required
  • Outcome-obsessed, pragmatic scientist who is relentlessly focused on creating positive customer impact

  • Culture of trust, empowerment and constructive feedback, open source commitment, 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 only 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.


Zalando is Europe’s leading online platform for fashion, 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 42+ million active customers through diverse skill-sets, interests and languages our teams choose to use.

Within Zalando Marketing Services (ZMS), you will work with many autonomous teams that live up to the standards of software craftsmanship, ownership and excellence. Along with our guiding set of principles we entrust you and your team to shape the future of Zalando.

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

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