Data Scientist Level 2

This is a combinatorial optimization challenge of significant scale. Every few minutes, the system must evaluate hundreds of incoming flights, assign transport devices (containers and bulk carts) to drivers, allocate baggage flows across multiple hall infeeds, and respect hard constraints such as hall capacity, driver availability, and flight departure times - while minimizing the risk and cost of mishandled baggage for passengers with short connections.

Manual planning and rule-based heuristics are no longer sufficient at this complexity and speed. As a Data Scientist on the BagPRO team, you will work on the mathematical optimization models that power this decision engine, improve solver performance (Gurobi), refine cost functions based on mishandled baggage risk, and translate operational constraints into model logic that drives measurable improvements in ground handling performance.

But it does not end there. You might work on an innovation project in KickstartAI (kickstartai.nl), contribute to our partnerships with various universities to drive innovation and research, or play a fundamental role in our community of practice for data science. What will you do?

• Develop and maintain mathematical optimization models that drive real-time baggage flow decisions at Amsterdam Airport Schiphol
• Improve and tune solver configurations (Gurobi) for performance under tight operational time constraints (solve cycles of 5-10 minutes)
• Refine objective functions and cost parameters based on mishandled baggage risk, passenger connection criticality, and operational priorities
• Translate complex ground handling constraints - hall capacities, driver availability, inbound flight schedules - into clean model logic
• Collaborate with operations managers, baggage flow controllers, and software engineers to validate model outputs against real-world outcomes
• Analyse post-hoc operational data to identify model gaps and continuously improve solution quality

• MSc in Operations Research, Applied Mathematics, Industrial Engineering, or a related quantitative field
• Hands-on experience ( with mathematical programming and combinatorial optimization (MIP, LP, CP, or similar)
• Proficiency with optimization solvers such as Gurobi, CPLEX, or OR-Tools
• Strong programming skills in Python, including experience with data processing libraries (pandas, NumPy)
• Ability to model ambiguous, real-world operational constraints rigorously and pragmatically
• Experience working with large, time-sensitive datasets and event-driven data pipelines is a plus
• Familiarity with airport or logistics operations is an advantage, but not a requirement

Competencies:

• Flexibility
• Expertise
• Problem analysis
• Teamwork
• Communication
• Result oriented
• Understanding of company processes
• Planning and organising, Wo

• A gross monthly salary between € 4.348,98 and € 6.212,83;
• Plenty of room for personal development and great advancement opportunities;
• Shift work allowance of up to 30%
• A good pension scheme;
• 8% holiday allowance;
• A year-end bonus of 8.33%;
• A profit-sharing scheme, up to 20%;
• 25 days of paid holiday and 5 additional days off;
• 7 extra days off it you work irregulard shift;
• Great discounts on airline tickets (after at least 6 months of employment);
• A sustainable transport policy - use all public transport for your commute within the Netherlands, free of charge;
• If your job allows: working from home and up to 8 weeks working abroad (within the EU and Caribbean);
• Discount of your health insurance;
• A personal budget for sustainable employability.

Amsterdam Airport Schiphol is one of Europe’s busiest hub airports, where KLM handles thousands of transfer passengers and their baggage every day. With over 70% of KLM traffic consisting of connecting passengers, the inbound baggage operation runs under constant time pressure: bags must be routed from arriving aircraft to the correct baggage halls, sorted, and loaded onto onward flights - all within tight connection windows.