Head of Applied Science & Engineering Ranking

We are looking for a Head of Applied Science to lead the scientific strategy and execution for Ranking across Europes largest fashion & lifestyle platform. This leader will define the long-term vision for Ranking relevance and visibility leveraging machine learning AI and experimentation to help millions of customers find the right products quickly and confidently.

You will lead a team of applied scientists driving the core ranking platform across Zalandos discovery surfaces. Partnering with Product Engineering Analytics and Design you will shape cross-surface experiences by balancing customer relevance commercial steering governance and marketplace health in line with Zalandos Platform-First strategy.

If you think you have what it takes we encourage you to apply even if you dont 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: WED LOVE YOU TO DO

• Define and drive the long-term Applied Science strategy aligning roadmap investments with customer needs and business priorities.
• Lead the development of advanced ML models for retrieval ranking and personalization to improve product ranking and discovery.
• Build and inspire a high-performing team of Applied Scientists fostering a culture of scientific rigor mentorship and continuous learning.
• Optimize for business outcomes by balancing relevance diversity monetization and operational efficiency.
• Accelerate innovation by investing in scalable machine learning platforms data infrastructure and experimentation frameworks.
• Establish best practices for model governance offline evaluation online experimentation and standardized scientific methods across teams.
• Drive adoption of emerging AI/ML techniques ensuring the organization stays at the forefront of technological advancements.
• Collaborate cross-functionally with Product Engineering UX and leadership to define priorities and influence the broader organizational strategy.