About the role
Discrete-event simulation, mathematical optimisation, and high-frequency sensor data from military platforms are your tools. The output is the maintenance schedule, the resource plan, the readiness picture that gives operational teams the confidence to act when it counts. You'll work in a cross-functional team building scalable data-driven products for mission readiness and capability management, where the gap between a good model and a trusted operational decision is yours to close.
What you'll do
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Design and develop discrete-event simulations using SimPy to model logistics chains, maintenance workflows, and operational bottlenecks across platform types.
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Build and solve optimisation models in IBM CPLEX to address scheduling, resource allocation, and planning challenges in real defence scenarios.
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Analyse high-volume sensor data from air, land, and sea platforms to extract signals, detect anomalies, and engineer features for predictive modelling.
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Validate models with domain experts, test their sensitivity to assumptions, and communicate uncertainty, trade-offs, and limitations to operational users.
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Write clean, modular Python code and work with data engineers, full-stack developers, and domain experts to move models from prototype into production.
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Contribute to internal tooling and knowledge-sharing so the team's analytical capabilities grow alongside yours.
What success looks like
Initially: you understand the operational context, the available data, and the real constraints — and you are already challenging assumptions alongside subject-matter experts.
As you take ownership: the simulation models reflect real operational constraints, not textbook ones. Optimisation models produce schedules and resource plans that practitioners recognise as usable, not just mathematically correct. Sensor pipelines you've built or improved are stable, tested, and easier for the next person to extend.
Over time: your ownership expands from individual models to the analytical patterns the whole team reaches for — tested, reusable, and faster for everyone.
What you bring
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Approximately five years of relevant applied experience in data science, operations research, or a closely related field — or equivalent depth through a PhD, industrial research, or another technically intensive pathway.
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Strong Python engineering skills, including modular design, automated testing, version control, and experience taking analytical code into production or production-like environments — with hands-on use of pandas, NumPy, and scikit-learn.
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Deep applied experience in at least two of the following: discrete-event simulation (SimPy preferred and in use here, though comparable tools transfer), mathematical optimisation (IBM CPLEX is the solver in use; Gurobi, OR-Tools, Pyomo, and other backgrounds are welcome), and sensor or time-series modelling (turning high-frequency, high-volume streams into reliable analytical outputs). You should be able and willing to contribute across the third area.
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Ability to translate ambiguous operational questions into concrete analytical approaches; comfortable working from messy real-world problems, not just well-specified ones.
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Professional working proficiency in Dutch and English; both are used day to day in technical discussions, documentation, and collaboration with domain experts.
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Eligibility to obtain and maintain the required national or NATO security clearance for this role. ILIAS Solutions supports candidates through the clearance process and is happy to explain what is involved before you apply.
Clearance eligibility, professional Dutch and English, and strong applied Python modelling experience are essential. We do not expect equal depth in simulation, optimisation, and sensor analytics — strong candidates may be deeper in two areas and ready to grow in the third.
Nice to have
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Background in logistics, maintenance planning, systems engineering, or defence operations; familiarity with the domain problems makes the modelling work faster and more credible.
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Experience packaging analytical components with Docker or comparable container technology.
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Experience processing large datasets with Spark, PySpark, or comparable distributed-data tools.
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Exposure to digital twins, operational dashboards, or predictive maintenance applications.
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Prior work in secure or regulated environments — defence, aviation, or similar.
Why ILIAS Solutions
ILIAS Solutions is part of the Patria Group and focuses on defence software where data quality and analytical rigour have direct operational consequences. The work is applied in the genuine sense: the models you build inform real decisions about platform availability and mission readiness, not internal performance reports.
The team is cross-functional by design — data scientists, data engineers, and full-stack developers working on shared products. That means your models get productionised, not shelved, and you'll see the feedback loop between analytical design and operational use play out in the same team. As your ownership grows, so does your influence over the analytical direction of the products — the path toward lead-level technical ownership is open.
Practicalities
ILIAS Solutions office, Almere, Netherlands.
Hybrid working with two days per week at the Almere office.
How to apply
Apply directly through this vacancy page. Questions before you apply? Reach out to Tim van Dam at [email protected] — he's happy to tell you more about the role and what the clearance process looks like in practice.
In defence operations, timing and reliable information shape every decision. Many organizations still rely on fragmented systems, limiting visibility and slowing execution. ILIAS Solutions connects logistics, maintenance, and operations into one integrated platform. This creates a single source of truth and enables teams to act on real time data across strategic, operational, and tactical levels. Large volumes of operational data move continuously between systems. This data reflects maintenance cycles, logistics flows, and asset readiness. Accuracy and consistency directly impact operational outcomes.
