Senior Data Scientist – MLOps & Forecasting

Data Scientist – Security Insights Team (Schiphol Group)

📍 Location: Schiphol, The Netherlands
🕓 Full-time | Hybrid work model

About the Team

At Schiphol Group, our Security Insights team turns data into actionable intelligence. We design and deliver data products that support aviation security operations—from daily planning to long-term strategic decision-making.

As demand for advanced analytics continues to grow, we’re scaling up our capabilities and laying the foundation for machine learning and AI-driven use cases across the airport. Join us in shaping the next chapter of data-driven innovation at one of Europe’s busiest airports.

About the Role

We are looking for a Data Scientist who will take end-to-end ownership of modelling and algorithm development for capacity forecasting within our Security Insights team.

Your first mission: design and deliver a robust, future-proof Security Capacity Demand Forecasting model. You’ll collaborate closely with analysts, developers, and business stakeholders, while also helping to define the roadmap for data science initiatives within the team.

This is an exciting opportunity for a hands-on data scientist who wants to make a tangible impact on how Schiphol manages and optimizes its security operations.

Key Responsibilities

• Lead the development and validation of statistical and forecasting models for operational capacity planning.

• Define, source, and validate data requirements for modelling use cases.

• Contribute to the creation of a scalable and reliable forecasting product.

• Work closely with the Data Analyst, Product Owner, and Business Analyst to align product goals and priorities.

• Communicate insights, findings, and model results to business stakeholders in a clear and actionable way.

• Collaborate with the Data & Analytics (DnA) platform team and other data science experts across Schiphol.

• Identify and prioritize new AI/ML use cases in aviation security.

• Support the implementation and productionization of models (ML Ops).

Tools & Technologies

• Python

• SQL

• Databricks

• Azure

• MLflow

What We’re Looking For

• Proven experience designing and implementing forecasting or statistical models.

• Experience with productionizing models and ML Ops principles.

• Strong Python programming skills and understanding of modelling best practices.

• Ability to translate complex technical concepts into clear business insights.

• Comfortable working independently in a cross-functional, agile team.

• Strong sense of ownership and capable of shaping the data science direction within the team.

• Experience working in agile environments.