Senior Principal Applied Scientist - Ranking and Recommendations (all genders)

Zalando Logistics Süd SE & Co. KG

Berlin Until 10/9/2026 8+ years exp First posted August 10, 2026 Last posted August 10, 2026
Job description

THE TEAM AND THE ROLE

The Ranking and Recommendations team at Zalando assists customers in their fashion discovery journey and helps them find what they are looking for. We are developing the next generation of our discovery systems, utilising state-of-the-art Deep Learning, Multi-Objective Optimization and Recommender Systems, to create highly personalised and engaging customer experiences by creating and optimizing ranking and recommendation algorithms for the Zalando customer experience.
As a Senior Principal Applied Scientist, you will address challenging fashion problems using cutting-edge approaches. You will contribute to drive our scientific roadmap, applying your scientific capabilities and in collaboration with a team of exceptional scientists and engineers developing models and cutting-edge machine learning products that enhance the experience of over 53 million customers

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

  • Utilise your extensive scientific experience in researching, deploying, and architecting machine learning and deep learning solutions, particularly in Deep Learning, Recommender Systems, Multi-objective optimisation to enhance the conversion journey of millions of our customers through product selection, ranking and recommendations. Collaborate with multiple teams to create a cutting-edge science stack that aligns with the product strategy and drive architectural and design principles.

  • Develop long-term roadmaps for research topics that span multiple product areas and address concrete business goals. As part of the leadership team, be the sparring partner and trusted advisor for Zalando senior leadership and advise on investments in people, strategic direction and make or buy decisions.

  • Act as a mentor and guide to senior and principal scientists, and actively share knowledge and expertise within Zalando’s science community.

  • Forge close partnerships with applied science leaders and product managers, to deliver state-of-the-art machine learning solutions to our customers and uncover new opportunities for growth. This includes driving and delivering large delivery projects with high uncertainty in production, applying the scientific approach to solve complex challenges, and communicating and aligning with other teams, stakeholders, and senior leadership. You will support and work on deploying, developing, implementing, testing, and researching ML models for personalisation, ranking, recommendations for the entirety of the Zalando customer journey.

WE'D LOVE TO MEET YOU IF

  • PhD in Machine Learning and AI (or equivalent experience), accompanied by 8+ years of industry experience, especially in deep learning, multi-armed bandits, or large-scale sequential decision-making systems, ideally applied to personalization, or ranking.

  • Experience leading and shaping the scientific roadmap of teams in products that span across multiple areas and working with senior Applied Science leaders. Ability to discover new business opportunities collaborating with product, science, and engineering managers and solve business problems through scientific solutions.

  • Extensive track record of addressing real-world problems using machine learning throughout the entire product cycle, and raises department and Zalando’s best practice in methods, algorithms and technology, including architecture, design patterns, and research roadmaps.

  • Authority on deep learning and extensive track record in leveraging research to advance the state of the art, including strong publication records.

  • Extensive hands-on experience in deep learning with proficiency in Python and frameworks like PyTorch or TensorFlow for RL implementations. Proficiency in cloud computing platforms such as AWS is also required.

PERKS AT WORK

  • 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 in selected 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 ZALANDO

Zalando is Europe’s leading online platform for fashion, connecting customers, brands and partners across 23 markets. We drive digital solutions for fashion, logistics, advertising and research, bringing head-to-toe fashion to more than 52 million active customers through diverse skill-sets, interests and languages our teams choose to use.

About this role

Summary

Lead machine learning research and deployment for ranking and recommendation systems at Zalando.

Job title

Senior Principal Applied Scientist - Ranking and Recommendations

Experience level

8+ years

Minimum experience

8+ years exp

Industry

fashion

Location requirements

Berlin-based role with flexible working options

Salary

Not specified

Management role

No

Skills & keywords

Required skills

phd in machine learning or AIpythonpytorch or tensorflowaws

Preferred skills

publication recordleadershipstrategic planning

Specializations

deep learningrecommender systemsrankingpersonalization
Locations

Structured locations inferred from the posting.

Berlin, Germany

Work arrangement unknown City
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