Data Scientist

Responsibilities: Design and conduct exploratory data analysis to identify new ideas, hidden patterns, and opportunities for air traffic optimization. As opposed to other domains, the quantity of data in the aeronautical space is not huge, therefore thinking outside of the traditional ML boxes is essential to propose new AI solutions specific to that field. Design, develop, and deploy ML models for real-time classification, regression, and sequence prediction using PyTorch, TensorFlow, or Scikit-learn, including transformer-based architectures for spatial-temporal problems Develop reinforcement learning agents using algorithms such as DQN, PPO, and Actor-Critic, and apply them to simulated and real-world environments via OpenAI Gym or custom setups.

Build and optimize RAG pipelines grounded on domain-specific documentation to support AI-generated reasoning and recommendations Evaluate LLM outputs for hallucination and groundedness, and develop domain-specific benchmarks to assess LLM reasoning in ATM contexts Build and automate machine learning pipelines using tools like Kubeflow, Airflow, or similar orchestration frameworks. Design reproducible workflows for data preprocessing, training, evaluation, and deployment. Integrate ML models into scalable APIs and deploy them to cloud-native environments using Docker and Kubernetes.

Monitor model performance over time, retrain, and iterate as needed based on live data and production drift. Maintain experiment tracking, model versio