Senior Data Scientist | Simulation, Optimization & Sensor Analytics

ph3Opportunity /h3 pAbout the roleDiscrete-event simulation, mathematical optimisation, and high-frequency sensor data from military platforms are your tools. bThe output is the maintenance schedule, the resource plan, the readiness picture /b 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.br/ /pbr/ h3Your Mission /h3 pbr/What you'll do /p ul liDesign and develop discrete-event simulations using SimPy to model logistics chains, maintenance workflows, and operational bottlenecks across platform types. /li liBuild and solve optimisation models in IBM CPLEX to address scheduling, resource allocation, and planning challenges in real defence scenarios. /li liAnalyse high-volume sensor data from air, land, and sea platforms to extract signals, detect anomalies, and engineer features for predictive modelling. /li liValidate models with domain experts, test their sensitivity to assumptions, and communicate uncertainty, trade-offs, and limitations to operational users. /li liWrite clean, modular Python code and work with data engineers, full-stack developers, and domain experts to move models from prototype into production. /li liContribute to internal tooling and knowledge-sharing so the team's analytical capabilities grow alongside yours. /li /ulbr/ What success looks likebInitially: /b you understand the operational context, the available data, and the real constraints — and you are already challenging assumptions alongside subject-matter experts.bAs you take ownership: /b 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.bOver time: /b your ownership expands from individual models to the analytical patterns the whole team reaches for — tested, reusable, and faster for everyone.br/ What you bringul liApproximately 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. /li liStrong 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. /li liDeep 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. /li liAbility to translate ambiguous operational questions into concrete analytical approaches; comfortable working from messy real-world problems, not just well-specified ones. /li liProfessional working proficiency in Dutch and English; both are used day to day in technical discussions, documentation, and collaboration with domain experts. /li liEligibility 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. /li /ulClearance 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 haveul liBackground in logistics, maintenance planning, systems engineering, or defence operations; familiarity with the domain problems makes the modelling work faster and more credible. /li liExperience packaging analytical components with Docker or comparable container technology. /li liExperience processing large datasets with Spark, PySpark, or comparable distributed-data tools. /li liExposure to digital twins, operational dashboards, or predictive maintenance applications. /li liPrior work in secure or regulated environments - defence, aviation, or similar. /li /ulbr/ h3Build Your Future with ILIAS /h3 pbr/Why ILIAS SolutionsILIAS 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.br/PracticalitiesbILIAS Solutions office, Almere, Netherlands. /bHybrid working with two days per week at the Almere office.br/ /p /p #J-18808-Ljbffr