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Zalando fulfills more than a hundred million customer orders every year and volumes are increasing. Fulfillment Core within Zalando builds and operates systems that are at the core of Zalando’s platform strategy and ensure timely, efficient, and correct delivery of every one of our customers’ 185+ million orders every year. Enabling the fulfillment of orders is a complex process, involving the choice of physical locations of our warehouses and the placement of stock, the planning of inner warehouse processes and capacity management, the management of a diverse range of fulfillment and delivery configurations, all while optimizing towards various competing targets. At the core of this system is demand forecasting. Accurate predictions of customer demand determine not just what to stock, but where and how to store and move inventory across our operations. We’re looking for a collaborative, thoughtful, and impact-driven Senior Applied Scientist to join our Fulfillment Core team. In this role, you’ll help build intelligent, large-scale forecasting models that influence how we plan, stock, and deliver fashion across Europe. You’ll work closely with other scientists, engineers, and business partners to co-create solutions that are both technically sound and deeply aligned with real-world needs.
Job Responsibility:
Collaborate with a cross-functional team to design and develop robust, scalable, and accurate demand forecasting models using machine learning and deep learning techniques
Work with engineering teams to bring models into production
Engage with stakeholders to understand challenges and identify how forecasting insights can empower better decisions
Design and improve models serving diverse forecasting use cases at different levels of granularity
Contribute to broader research efforts across the business unit
Engage with Zalando's growing community of Applied Scientists through reading groups, knowledge-sharing sessions, and mentoring opportunities
Requirements:
Hands-on extensive experience in time-series forecasting within an industrial setting. Experience with large-scale forecasting applications is highly desirable
Solid understanding of machine learning and deep learning techniques applied to forecasting problems
Academic background in a quantitative field such as computer science, engineering, mathematics, statistics, or a related discipline
Proficiency in Python and ML libraries and frameworks such as PyTorch, TensorFlow, LightGBM, Scikit-learn, Pandas, and NumPy
Familiarity with data-intensive systems and ML engineering tools, including PySpark, Databricks, AWS (e.g. SageMaker, S3), Airflow, MLflow, CI/CD, Docker, Git, and model versioning tools
A proactive and self-driven mindset with a strong sense of ownership and accountability
A team player with excellent collaboration skills who enjoys working with stakeholders to solve real-world challenges
Comfort working in a diverse, inclusive, and international environment where English is the main language
What we offer:
27 days of holiday a year to start for full-time employees (+1 day for every calendar year up to 30 days)
2 paid volunteering days a year
Hybrid working model with up to 60% remote per week
Work from abroad for up to 30 working days a year
Employee shares program
40% off fashion and beauty products sold and shipped by Zalando, 30% off Lounge by Zalando, discounts from external partners
Relocation assistance available (subject to prior agreement)
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