Senior Applied Scientist

Agentic Systems is building Zalando Assistant, our own agentic fashion assistant, with the goal of making it the starting point for fashion. We combine state-of-the-art machine learning (including GenAI/LLMs, agentic systems, personalization, and neural search) with Zalando’s catalog, data, and fashion expertise to deliver an experience that inspires customers in a new way. The team has grown, and we’re looking for a Senior Applied Scientist to lead research-related projects and raise our best practices. You will take on the hardest scientific problems in the product, set technical direction across teams, and work with a group of exceptional scientists and engineers to build machine learning products that improve the experience of millions of customers.

• Lead research projects end to end, from problem framing and approach to experimentation, production deployment, and monitoring. Shape the science stack for Zalando Assistant so that it aligns with product strategy, and drive architectural and design principles across teams.
• Define and drive the scientific roadmap for complex fashion and agentic-assistant challenges, ensuring we apply state-of-the-art solutions where they create customer value. Contribute to our scientific standing through internal peer reviews and, where appropriate, external publications at top-tier conferences and journals.
• Raise the bar for how the team works: set best practices in experimentation, evaluation, and statistical rigor, and make sure results reflect real improvements rather than noise.
• Mentor scientists across the team, review their technical work, and share knowledge with Zalando’s wider science community.
• Partner closely with applied science leaders, engineering, and product managers to deliver machine learning solutions to our customers and uncover new opportunities for growth.

• PhD in NLP, deep learning, search & recommendation, or a related field (or equivalent experience), 6+ years of industry experience, including leading research or applied science work at scale.
• Experience in developing long-term research roadmaps and applying scientific methods to tackle research problems. A strong inclination to collaborate with product, science, and engineering managers and solve business problems through scientific solutions.
• Extensive track record of addressing large-scale real-world problems using machine learning throughout the entire product cycle, encompassing problem analysis, data processing, literature review, production deployment, monitoring, and maintenance.
• Strong publication records at top-tier venues such as SIGIR, KDD, WWW, ICLR, NeurIPS, ICML, JMLR, etc.
• Extensive hands-on experience in deep learning with proficiency in Python and frameworks like PyTorch or TensorFlow.