Data Scientist

Background :

• We are looking for a highly motivated Data Scientist, with expertise in Operations Research and Python development, who will play a key role in shaping the logistics of fulfilling customer orders . The Data Scientist will be part of the Order Allocation area, in the Fulfilment and Core Services domain.
• The team is cross-functional and builds digital products that orchestrate how and when customer orders are fulfilled, balancing various trade-offs and optimizing different objectives. In addition to software, the team provides insights to business partners on product configurations and logistic process improvements at a global scale, in countries where present.
• The Data Scientist will closely work with other data scientists, product owners, and software engineers, in the development of digital products in order allocation. This will require:
• Translating business challenges into optimization ideas together with a cross-functional team.
• Designing and implementing models to improve order allocation and fulfillment efficiency.
• Maintaining and improving existing optimization models in production.

Requirements:

• The ideal candidate should possess strong technical expertise, analytical skills, business curiosity and proactive communication. In particular:
• Strong background in Operations Research (e.g., mixed-integer programming, sensitivity analysis, Pareto frontier, etc.)
• Expertise in Python development (e.g., large codebases, CI/CD, monitoring, etc.)
• Proficiency with Google Cloud Platform: Big Query, Cloud Run, Cloud Monitoring etc.
• Proactive attitude and critical thinking
• Team spirit and ability to work in cross-functional teams with product owners, data scientists, and software engineers.
• Bright & Analytical – Quickly understands complex decision problems and finds efficient solutions.
• Pragmatic & Business-Oriented – Focuses on delivering impactful solutions rather than over-engineering.
• Proactive & Self-Starter – Takes initiative and drives projects forward without needing close supervision.