What're Looking For We are looking for a Data Scientist, Aerospace with strong ownership of complex model development, multi-sensor analytics, and end-to-end integration for autonomous drones and robotics.
This role owns larger technical work packages, identifies data and model risks earlier, and guides junior contributors through practical delivery.
You will help move models toward repeatable, test-ready AI capabilities that perform reliably across diverse operational conditions.
What You'll Do - Lead defined AI/ML work packages spanning data strategy, modelling, integration, evaluation, deployment, monitoring, and technical documentation.
- Design perception and sensor-fusion pipelines combining RGB, thermal, depth, LiDAR, radar, IMU, GNSS, telemetry, and environmental data.
- Select and implement appropriate methods for object detection, segmentation, tracking, pose estimation, localization, mapping support, anomaly detection, forecasting, and decision support.
- Define dataset coverage plans and simulation or synthetic-data strategies for rare events, difficult environments, and safety-related scenarios.
- Lead error analysis, ablation studies, uncertainty evaluation, robustness testing, and model-performance trade studies.
- Optimize models for GPU, NPU, CPU, or embedded platforms using quantization, pruning, compilation, batching, and architecture selection as appropriate.
- Coordinate integration with autonomy, controls, embedded, cloud, robotics middleware, ground-control, and user-application teams.
- Establish reproducible training, versioning, experiment-tracking, validation, and release practices for assigned solutions.
- Investigate data drift, sensor failures, model degradation, false detections, localization errors, and field-test observations using structured root-cause analysis.
- Mentor Associate, interns, and research contributors.
Experience You'll Bring - 4-6 years of professional experience in data science, AI/ML, perception, sensor fusion, autonomous systems, or related engineering.
- Demonstrated ownership of models or data products from requirements through integration and validation.
- Strong experience with Python, deep-learning frameworks, data pipelines, experiment tracking, version control, and Linux environments.
- Practical experience with multi-modal data, computer vision, sensor fusion, robotics logs, edge inference, or simulation with ROS2 framework.
- Understanding of Kalman filtering, Bayesian estimation, tracking, coordinate transformations, time synchronization, Path planning, navigation and uncertainty is strongly preferred.
- Experience with MLOps, containers, CI/CD, model registries, cloud platforms, or embedded deployment is an asset.
- Experience mentoring junior contributors and communicating technical trade-offs to senior stakeholders.
#LI-RJ1 #LI-Hybrid
