Senior Applied Scientist - Fulfilment Planning (all genders) [LS]

at  Zalando

THE ROLE & THE TEAM

Do you feel happy when shopping online and your order arrives the very next day, all articles in a single box? And do you get frustrated when every article arrives separately, some even late? We do as well! And our team - Fulfillment Planning - is tackling exactly this challenge. We build the software that decides how fashion items are moving inside Zalando’s Europe-wide logistics network and how they reach your home.
As a Senior Applied Scientist in the Fulfillment Planning team in Berlin, you will work on the core system and algorithms that control the flow of fashion items through our Europe-wide logistics network. By applying operational research and machine learning techniques, you will explore new ways to speed up deliveries, increase sustainability and reduce logistic costs. Within a multidisciplinary team of research and engineering, you will work on highly critical systems to the business.

WHAT WE’D LOVE YOU TO DO (AND LOVE DOING)

• Drive innovation in algorithm development to optimise the balance between delivery speed and cost-efficiency across our logistics network. Collaborate with cross-functional teams to incorporate cutting-edge scientific methodologies into live business operations, ensuring a continuous flow of improvements.
• You and your team will work like a startup and take ownership for the whole research and development cycle - from data collection and architecture design to testing, implementation and maintenance.
• Design and implement advanced, data-driven algorithms to optimise resource allocation and order fulfilment. Build and refine allocation models that mimic the intricate behaviour of our logistics network. These algorithms will factor in macro-level constraints such as warehouse configurations, transportation capacity, inventory distribution, and order routing strategies to improve the accuracy of promises and planning.
• Work closely with product managers, data analysts, and engineers, translating business requirements into scientific problems and delivering innovative solutions.
• Lead data-driven analyses aimed at improving key performance indicators (KPIs) and operational efficiency. Oversee the end-to-end experimentation process to propose and validate enhancements to our planning algorithms, continuously improving their accuracy and effectiveness.