Data Scientist at Thales
Summary
Build and deploy AI/ML models for aeronautical domains, including transformers, reinforcement learning, and RAG pipelines, to optimize air traffic and support decision-making.
Data Scientist – Thales – Changi, Singapore
Full‑time, on‑site position in Changi, Singapore. Thales is a global technology leader trusted by governments, institutions and enterprises to tackle demanding challenges across quantum applications, AI, cybersecurity, 6G innovation, aerospace, space, cybersecurity and digital identity. Operating on the forefront of aerospace and space, Thales has been a trusted partner in Singapore since 1973, delivering cutting‑edge solutions across aerospace (including air traffic management), defence, security and cybersecurity sectors. This role offers great opportunities for career growth as you help enable critical decisions rooted in human intelligence and shape the future. Responsibilities
Design and conduct exploratory data analysis to identify new ideas, hidden patterns, and opportunities for air traffic optimization. Think outside the traditional ML boxes to propose new AI solutions for aeronautical domains, where data volume is limited. 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 optimise 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 versioning and reproducibility using tools like MLflow or Weights & Biases. Collaborate with DevOps and backend engineers to ensure seamless integration of ML components into larger systems. Requirements
Education: Bachelors in Computer Science or Information Technology. A Masters degree in Computer Science or Data Science is an advantage. Domain & Data: Aeronautical domain knowledge is a major plus. Experience in domains with limited historical data is also beneficial. Data: Good understanding of statistics, features and analytics and how to map them to the domain. Mathematics & Algorithms: High level of core mathematical skills required for prediction algorithms and optimisation. Core ML experience: 3‑5 years delivering end‑to‑end ML projects from data prep to production. Proficiency in Python and frameworks such as PyTorch or TensorFlow. Deep learning: Familiarity with modern architectures like transformers, attention mechanisms or encoder‑decoder models. LLM & RAG: Experience building and optimising RAG pipelines, prompt engineering and agentic frameworks such as LangChain or LlamaIndex, evaluating LLM outputs for hallucination and groundedness, and designing domain‑specific benchmarks. Reinforcement Learning: Solid understanding of RL theory and experience with at least one RL algorithm (e.g. PPO, DQN). Practical experience with OpenAI Gym, Gymnasium or equivalent RL environments. MLOps & Engineering: Strong grasp of MLOps practices, including Kubeflow Pipelines, MLflow, Docker and Kubernetes. Experience with ETL/ELT tooling (Apache Spark) and modern data‑warehouse/lake (S3‑based). Knowledge of CI/CD principles for ML workflows and model promotion strategies. Desirable: Working knowledge of other languages (Python3, Scala, Go, TypeScript, C/C++17, Java17). Familiarity with designing or implementing AI/MLOps pipelines in public cloud (Azure, AWS, GCP). Traits: Learning agility, flexibility, pro‑activity, comfortable with agile teamwork and user engagement. At Thales, we’re committed to fostering a workplace where respect, trust, collaboration and passion drive everything we do. Here, you’ll feel empowered to bring your best self, thrive in a supportive culture and love the work you do. Join us, and be part of a team reimagining technology to create solutions that truly make a difference – for a safer, greener and more inclusive world.
Full‑time, on‑site position in Changi, Singapore. Thales is a global technology leader trusted by governments, institutions and enterprises to tackle demanding challenges across quantum applications, AI, cybersecurity, 6G innovation, aerospace, space, cybersecurity and digital identity. Operating on the forefront of aerospace and space, Thales has been a trusted partner in Singapore since 1973, delivering cutting‑edge solutions across aerospace (including air traffic management), defence, security and cybersecurity sectors. This role offers great opportunities for career growth as you help enable critical decisions rooted in human intelligence and shape the future. Responsibilities
Design and conduct exploratory data analysis to identify new ideas, hidden patterns, and opportunities for air traffic optimization. Think outside the traditional ML boxes to propose new AI solutions for aeronautical domains, where data volume is limited. 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 optimise 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 versioning and reproducibility using tools like MLflow or Weights & Biases. Collaborate with DevOps and backend engineers to ensure seamless integration of ML components into larger systems. Requirements
Education: Bachelors in Computer Science or Information Technology. A Masters degree in Computer Science or Data Science is an advantage. Domain & Data: Aeronautical domain knowledge is a major plus. Experience in domains with limited historical data is also beneficial. Data: Good understanding of statistics, features and analytics and how to map them to the domain. Mathematics & Algorithms: High level of core mathematical skills required for prediction algorithms and optimisation. Core ML experience: 3‑5 years delivering end‑to‑end ML projects from data prep to production. Proficiency in Python and frameworks such as PyTorch or TensorFlow. Deep learning: Familiarity with modern architectures like transformers, attention mechanisms or encoder‑decoder models. LLM & RAG: Experience building and optimising RAG pipelines, prompt engineering and agentic frameworks such as LangChain or LlamaIndex, evaluating LLM outputs for hallucination and groundedness, and designing domain‑specific benchmarks. Reinforcement Learning: Solid understanding of RL theory and experience with at least one RL algorithm (e.g. PPO, DQN). Practical experience with OpenAI Gym, Gymnasium or equivalent RL environments. MLOps & Engineering: Strong grasp of MLOps practices, including Kubeflow Pipelines, MLflow, Docker and Kubernetes. Experience with ETL/ELT tooling (Apache Spark) and modern data‑warehouse/lake (S3‑based). Knowledge of CI/CD principles for ML workflows and model promotion strategies. Desirable: Working knowledge of other languages (Python3, Scala, Go, TypeScript, C/C++17, Java17). Familiarity with designing or implementing AI/MLOps pipelines in public cloud (Azure, AWS, GCP). Traits: Learning agility, flexibility, pro‑activity, comfortable with agile teamwork and user engagement. At Thales, we’re committed to fostering a workplace where respect, trust, collaboration and passion drive everything we do. Here, you’ll feel empowered to bring your best self, thrive in a supportive culture and love the work you do. Join us, and be part of a team reimagining technology to create solutions that truly make a difference – for a safer, greener and more inclusive world.