Fleet Scheduling Data Scientist

Quick Facts

• Role: Fleet Scheduler Data Scientist

Description

You will design, build, and operationalize AI/ML algorithms that optimize and control a satellite/ground fleet scheduling system. The work includes predictive planning and reactive real-time control loops using large-scale combinatorial optimization on heterogeneous graphs, along with physics-informed ML models trained on simulated spacecraft data and telemetry. You will partner with software and engineering teams to implement scalable prediction/optimization/control architectures and ensure production-grade delivery.

Responsibilities

• Own the architecture of AI/ML-powered satellite and ground scheduling algorithms
• Design and deploy large-scale combinatorial optimization algorithms (MIP, heuristics, and RL-based solvers)
• Apply learning-augmented planning, graph-based scheduling, and computational efficiency techniques
• Create a multi-tier scheduling system from high-level satellite activities to mid-level configuration control
• Develop physics-informed ML models for predicting satellite behaviors using simulation and telemetry data
• Deliver production-grade models/optimizers with end-to-end pipelines for training, validation, deployment, monitoring, and drift management
• Partner with satellite engineers to develop simulated training data and define key system physics
• Incorporate fleet modeling into design analysis and trades
• Identify and drive new AI/ML opportunities across design, manufacturing, and in-orbit operations

Requirements

• BS/MS/PhD in Computer Science, Electrical Engineering, Applied Math, Physics, Aerospace, or related field, or equivalent experience
• 5–7+ years delivering AI/ML and other optimization systems in production
• Demonstrated experience with large-scale combinatorial optimization (scheduling/resource allocation/logistics/network capacity)
• Strong traditional optimization background (mixed-integer programming or constraint programming)
• Experience modeling and solving large-scale graph or heterogeneous-graph problems
• Strong Python and ML/optimization tooling (e.g., PyTorch)
• Experience building data pipelines and deploying models into operational systems

Benefits

Equal Opportunity at-will employer (employment governed by merit, competence, and qualifications).