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The ZEOS department is responsible for all partner-facing Zalando Logistics Solutions. We provide a holistic approach to delivering the fulfillment solutions that meet our partner’s needs by unifying these services under a single umbrella. We aim to provide our partners with a profitable fulfillment experience, and we see that to do this, Machine Learning, Operations Research, and data-driven solutions will play a pivotal role. We are seeking a highly motivated Junior Applied Scientist who is fueled by the desire to kickstart their career by building innovative and impactful ML/Optimization systems for our B2B logistics partners. You will join an established team of Senior Applied Scientists and Machine Learning Engineers, working in a cross-functional setup with Product Managers, Data Engineers, and Software Engineers. The team’s focus is helping our B2B partners improve inventory health and order fulfillment efficiency. With guidance and mentorship from senior team members, you will contribute to building various ML/DL forecasting models (demand, returns, lead-times), stochastic inventory optimization solutions, recommendation services, and emerging Agentic AI systems. You will play a key role in a cross-functional team where your eagerness to learn and grow will directly translate into value for our partners!
Job Responsibility
Contribute to building various ML/DL forecasting models (demand, returns, lead-times), stochastic inventory optimization solutions, recommendation services, and emerging Agentic AI systems
Be an active scientific partner, working alongside experienced scientists and product teams to define problems and build solutions
Tackle cutting-edge challenges in stochastic inventory optimization, multi-echelon demand forecasting, and recommender systems
Assist in defining how we measure performance of MCP servers
Contribute to implementing evaluation frameworks (e.g., LLM-as-a-judge, safety guardrails) to ensure autonomous systems act reliably
Follow ideas from initial research and prototyping to production
Learn how to define success metrics, collaborate with engineers to deploy scalable services, and monitor real-world impact on partner KPIs
Build platform-level services that scale across hundreds of diverse partners
Requirements
Educational background in a quantitative field
Masters degree or higher preferred
Up to 2 years of hands-on experience (including internships, working student roles, or significant academic/thesis projects) applying scientific methods to solve complex problems
A solid theoretical foundation and practical exposure in at least one of the following areas: Machine Learning or Deep Learning for time-series forecasting (e.g., LGBM, ARIMA, Prophet, Transformers)
Operations Research and Optimization (e.g., stochastic inventory models, linear/integer programming, Monte Carlo simulations)
Proficiency in SQL and Python, with some exposure to working with datasets
Strong communication skills with the ability to explain scientific concepts clearly and a desire to learn how to communicate effectively with business stakeholders
A collaborative, growth-oriented mindset. You have a passion for learning by doing and are not afraid to ask questions
You thrive in our team's core value: “High Challenge, High Support”! We cherish an open, direct feedback culture and are here to help you develop into a world-class scientist.
What we offer
Employee shares program
40% off fashion and beauty products sold and shipped by Zalando, 30% off Lounge by Zalando, discounts from external partners
2 paid volunteering days per year
27 days of vacation a year to start for full-time employees
Family services, including counseling and support
Health and wellbeing options (including Wellhub, formerly Gympass)
Mental health support and coaching available
Drive your development through our training platform and biannual peer-to-peer review