Opportunity
The RoleWe’re looking for a Senior Data Scientist with strong skills in simulation modeling, mathematical optimization, and sensor data analysis. This is a hands-on, high-impact role where you will develop analytical solutions for real-world defense scenarios — from optimizing aircraft maintenance schedules to analyzing live sensor data from naval vessels.
You’ll be joining a cross-functional team that builds scalable, data-driven products supporting mission readiness and capability management. If you enjoy combining discrete-event simulation, CPLEX optimization, and sensor analytics to answer hard operational questions, this job will suit you well.
Your mission
Key Responsibilities:Simulation Modeling
- Design and develop discrete-event simulations using SimPy (or similar) to model logistics chains, maintenance workflows, and operational bottlenecks.
- Build simulation components that reflect real-world systems, allowing for stress testing and what-if scenarios.
- Build and solve optimization models using IBM CPLEX.
- Translate complex operational needs — such as scheduling, resource allocation, or planning — into mathematical formulations.
- If you’ve used Gurobi or other solvers before, that's fine — but you’ll need to work with CPLEX here.
- Analyze high-volume, high-frequency sensor data from military platforms (air, land, sea).
- Extract signals, detect anomalies, and engineer features for predictive modeling.
- Combine time-series analytics with simulation and optimization outputs to inform decision-making.
- Write clean, modular Python code with attention to performance and reproducibility.
- Collaborate with data engineers, full-stack developers, and domain experts to productionize your models.
- Share knowledge across teams and contribute to internal tooling and frameworks.
Your capabilities
What We’re Looking For/ Required Skills- 5+ years of experience in Data Science, Operations Research, or a similar role.
- Strong coding skills in Python, including libraries like pandas, NumPy, matplotlib, scikit-learn.
- Solid experience with SimPy or any other discrete-event simulation library or tool.
- Practical experience formulating and solving optimization models with IBM CPLEX (knowledge of other solvers is welcome, but CPLEX is what we use).
- Experience working with sensor or time-series data in an applied setting.
- Strong problem-solving mindset — you enjoy turning ambiguous situations into concrete analytical approaches.
- Background in logistics, maintenance planning, systems engineering, or defense operations.
- Experience with containerized development (Docker) or distributed data tools (e.g., Apache Spark).
- Exposure to digital twins, operational dashboards, or predictive maintenance applications.
- Familiarity with working in secure or regulated environments (defense, aviation, etc.).
Land @ILIAS
Exited and recognizing yourself in the description?
Let our HR team know and send your CV to [email protected]
we are thrilled to get to know you!
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