Develop analytical solutions for real-world defense scenarios, optimizing aircraft maintenance schedules and analyzing live sensor data from naval vessels for ILIAS Solutions.
Requirements
- 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.
- 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.).
Responsibilities
- 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.
- 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.
Other
- 5+ years of experience in Data Science, Operations Research, or a similar role.
- Strong problem-solving mindset — you enjoy turning ambiguous situations into concrete analytical approaches.
- Background in logistics, maintenance planning, systems engineering, or defense operations.
- Eligibility for a U.S. security clearance.
- U.S. citizenship is a legal requirement.
