THE ROLE & THE TEAM
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.
You’ll find our Berlin campus and HQ in the vibrant area of Friedrichshain. With amazing views of the Spree river and East Side Gallery, it’s a campus designed to inspire. Berlin is the city that has shaped our business. Are you up for a new experience in one of the most vibrant, diverse, and green cities in Europe?
INCLUSIVE BY DESIGN
If you think you have what it takes, we encourage you to apply even if you don't 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: https://jobs.zalando.com/en/our-culture/diversity-and-inclusion
WHAT WE’D LOVE YOU TO DO (AND LOVE DOING)
Collaborate with a cross-functional team of applied scientists, engineers, and analysts within the Fulfillment Core to design and develop robust, scalable, and accurate demand forecasting models using machine learning and deep learning techniques.
Work hand-in-hand with engineering teams to bring your models into production - ensuring they’re robust, maintainable, and responsive to evolving needs.
Engage openly with stakeholders to understand the challenges they face and identify how forecasting insights can empower better decisions.
Design and improve models serving diverse forecasting use cases at different levels of granularity, such as inventory allocation and predictive storage management.
Contribute to broader research efforts across the business unit, sharing insights and collaborating on exploratory initiatives.
Engage with Zalando’s growing community of Applied Scientists through reading groups, knowledge-sharing sessions, and mentoring opportunities.
WE’D LOVE TO MEET YOU IF…
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.
OUR OFFER
Zalando provides a range of benefits, here’s an overview of what you can expect. Ask your Talent Acquisition Partner to learn more about 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, actual practice is up to each team to best support their collaboration
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
