{"id":171966,"url":"https://alion.io/job/thales-group-data-scientist","title":"Data Scientist","company":{"id":143,"name":"Thales Group","domain":"thalesgroup.com","url":"https://alion.io/company/thales","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":78,"open_postings":218,"ghost_share":0.018,"stale_share":0.826,"repost_share":0.018,"time_to_fill_p50_days":48,"computed_at":"2026-10-25T05:49:26Z"}},"role":"Data Science","role_family":"Data Science","seniority":null,"employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Singapore"],"countries":["SG"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":48000,"max_usd":115000,"period":"year","method":"role_country_seniority_unknown","sample_n":97},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Airflow","optional":false},{"name":"Docker","optional":false},{"name":"Hallucination","optional":false},{"name":"Kubeflow","optional":false},{"name":"Kubernetes","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"OpenAI","optional":false},{"name":"PPO","optional":false},{"name":"PyTorch","optional":false},{"name":"RAG","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Scikit-learn","optional":false},{"name":"TensorFlow","optional":false},{"name":"TensorFlow C++","optional":false},{"name":"Weights & Biases","optional":false},{"name":"AI Agents","optional":true},{"name":"Amazon S3","optional":true},{"name":"AWS","optional":true},{"name":"Azure","optional":true},{"name":"C++","optional":true},{"name":"CI/CD","optional":true},{"name":"Encoder-Decoder","optional":true},{"name":"ETL/ELT","optional":true},{"name":"GCP","optional":true},{"name":"Go","optional":true},{"name":"Gymnasium","optional":true},{"name":"LangChain","optional":true},{"name":"LlamaIndex","optional":true},{"name":"Prompt Engineering","optional":true},{"name":"Python","optional":true},{"name":"Spark","optional":true},{"name":"TypeScript","optional":true}],"status":"live","first_seen_at":"2026-07-09T00:00:00Z","employer_posted_date":"2026-07-09","last_verified_at":"2026-10-24T17:33:25Z","board_verified":true,"closed_at":null,"days_open":108,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":108},"description":"Location: Singapore, SingaporeThales is a global technology leader trusted by governments, institutions, and enterprises to tackle their most demanding challenges. From quantum applications and artificial intelligence to cybersecurity and 6G innovation, our solutions empower critical decisions rooted in human intelligence. Operating at the forefront of aerospace and space, cybersecurity and digital identity, we’re driven by a mission to build a future we can all trust.\nIn Singapore, Thales has been a trusted partner since 1973, originally focused on aerospace activities in the Asia-Pacific region. With 2,000 employees across three local sites, we deliver cutting-edge solutions across aerospace (including air traffic management), defence and security, and digital identity and cybersecurity sectors. Together, we’re shaping the future by enabling customers to make pivotal decisions that safeguard communities and power progress.Responsibilities:\nDesign and conduct exploratory data analysis to identify new ideas, hidden patterns, and opportunities for air traffic optimization.\nAs 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.\nDesign, 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\nDevelop 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.\nBuild and optimize RAG pipelines grounded on domain-specific documentation to support AI-generated reasoning and recommendations\nEvaluate LLM outputs for hallucination and groundedness, and develop domain-specific benchmarks to assess LLM reasoning in ATM contexts\nBuild and automate machine learning pipelines using tools like Kubeflow, Airflow, or similar orchestration frameworks.\nDesign reproducible workflows for data preprocessing, training, evaluation, and deployment.\nIntegrate ML models into scalable APIs and deploy them to cloud-native environments using Docker and Kubernetes.\nMonitor model performance over time, retrain, and iterate as needed based on live data and production drift.\nMaintain experiment tracking, model versioning, and reproducibility using tools like MLflow or Weights & Biases.\nCollaborate with DevOps and backend engineers to ensure seamless integration of ML components into larger systems.\nRequirements:\nEducation\nBachelors in Computer Science or Information Technology\nMasters degree in Computer Science or Data Science, if applicable\nEssential Skills/Experience\nDomain & DataAeronautical domain knowledge would be a major plus. At minimum, some experience in a domain that is not one of the common ones (vision, chatbots...).\nExperience in a domain where available historical data is not huge would be a plus.\nGood understanding of data (statistics, features, analytics) and how to map them to the domain.\nHigh level of core mathematic skills: algorithms (in particular for prediction), optimizations.\n\nCore ML3-5 yrs delivering ML projects end-to-end (data prep to production).\nProficiency in Python and machine learning frameworks such as PyTorch or TensorFlow.\nFamiliarity with modern deep learning architectures - including transformers, attention mechanisms, or encoder-decoder models.\n\nLLM & RAGExperience building and optimizing RAG pipelines, including chunking strategies, retrieval tuning, and context management\nProficiency in prompt engineering and agentic frameworks such as LangChain or LlamaIndex\nPractical experience evaluating LLM outputs - including hallucination detection, and groundedness against source material\nExperience designing domain-specific benchmarks to evaluate LLM reasoning and knowledge in specialized fields\n\nReinforcement LearningSolid understanding of reinforcement learning theory and experience with at least one RL algorithm (e.g. PPO, DQN).\nPractical experience with OpenAI Gym, Gymnasium, or equivalent RL environments.\n\nMLOps & EngineeringStrong grasp of MLOps practices, including the use of Kubeflow Pipelines, MLflow, and model serving platforms.\nExperience deploying models in containerized environments using Docker and Kubernetes.\nHands-on with ETL/ELT tooling (Apache Spark) and modern data-warehouse/lake (S3-based).\nKnowledge of CI/CD principles for machine learning workflows and model promotion strategies.\nAbility to build and debug data pipelines that support both training and inference workloads.\n\nDesirable Skills/Experience\nWorking knowledge of other languages (e.g., Python3, Scala2 or Scala3, Go, TypeScript, C, C++17, Java17)\nFamiliar with designing and/or implementing AI/MLOps pipelines in public cloud (e.g., Azure, AWS, GCP)\nEssential / Desirable Traits\nPossess learning agility, flexibility and pro-activity\nComfortable with agile teamwork and user engagement\nAt 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.","description_format":"text","description_chars":5453,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[{"name":"Singapore","iso":"SG","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Satellite Communications","Satellite Manufacturing","Electronic Warfare","Information Security"],"lifecycle":[{"event":"open","at":"2026-08-30T01:36:04Z"}],"visa":[],"liveness":{"score":8,"band":"cold","label":"Long shot","p_open":1,"p_active":0.297,"p_room":0.28,"age_days":108,"expected_fill_days":48,"reasons":["conf:12","stale_co","velocity","win:tail","crowd:brand"],"computed_at":"2026-10-25T05:49:26Z"},"pay":null,"html_url":"https://alion.io/job/thales-group-data-scientist","json_url":"https://alion.io/job/thales-group-data-scientist.json","meta":{"generated_at":"2026-10-25T08:30:35Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":12074,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-26T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":143},"rest":"https://alion.io/mcp/rest/get_company?id=143"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fthales-group-data-scientist"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fthales-group-data-scientist"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fthales-group-data-scientist"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/thales-group-data-scientist\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fthales-group-data-scientist"}]}