{"id":1253603,"url":"https://alion.io/job/astar-leadsenior-research-engineer","title":"Lead/Senior Research Engineer","company":{"id":50235,"name":"Agency for Science, Technology and Research","domain":"a-star.edu.sg","url":"https://alion.io/company/a-star-edu","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"SuccessFactors","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"lead","employment_type":null,"work_mode":"on_site","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":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Apache Kafka","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Dask","optional":false},{"name":"dbt","optional":false},{"name":"Digital Twin","optional":false},{"name":"Docker","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Kubernetes","optional":false},{"name":"Pandas","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Semantic Search","optional":false},{"name":"Semantic Search","optional":false},{"name":"SQL","optional":false},{"name":"Time Series Forecasting","optional":false},{"name":"Agentic Workflows","optional":true},{"name":"LLM","optional":true},{"name":"Prompt Engineering","optional":true},{"name":"Spark","optional":true}],"status":"live","first_seen_at":"2026-09-25T18:38:00Z","employer_posted_date":"2026-09-25","last_verified_at":"2026-10-01T18:38:26Z","board_verified":true,"closed_at":null,"days_open":6,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":6},"description":"Job Description\nThe Digital Supply Chain Group at the Digital Manufacturing Division, ARTC, is seeking a Lead/Senior Data Engineer with strong expertise in data pipelines, transformation, analytics, Gen-AI enablement, and workflow automation. This role supports ongoing and new AI research efforts focused on supply chain analysis, intelligent automation, and decision-support systems. The successful candidate will play a pivotal role in preparing clean, structured, and timely data; developing automation-ready data services; and enabling AI-driven and Gen-AI-enabled solutions for real-world supply chain challenges in FMCG, Med-tech, manufacturing, aerospace, energy, and semiconductor sectors.\nKey Responsibilities\nBuild and Maintain Data InfrastructureDesign, implement, and maintain scalable ELT/ETL pipelines across diverse data sources (SAP, MES, WMS, ERP, IoT, etc.)\nDevelop automated data ingestion and transformation processes using modern tools (e.g., Airflow, dbt, Kafka, etc.)\n\nData Modeling & Analytics SupportPerform data wrangling and preprocessing tailored for ML/AI model training and simulation environments\nWork with AI scientists to prepare datasets for time-series forecasting, optimization models, simulation environments, and Gen-AI applications such as retrieval-augmented generation, knowledge assistants, and automated reporting\nStructure and curate domain knowledge, metadata, and enterprise data assets to support reliable Gen-AI workflows, including prompt-ready datasets, semantic search, and knowledge-base development\n\nCollaborate Across DomainsLiaise with domain experts, supply chain analysts, and software developers to understand operational data needs\nServe as the bridge between raw data and AI solution pipelines\nTranslate business and research requirements into automated workflows that connect data ingestion, analytics, model outputs, user interfaces, and operational decision processes\n\nMaintain Data Quality & GovernanceImplement checks, logging, and alerts to ensure high data reliability and traceability\nEnsure alignment with FAIR data principles and secure data handling practices\nEstablish data lineage, validation, access control, and monitoring practices required for production-grade AI and Gen-AI solutions\n\nTooling and DeploymentDevelop containerized and cloud-compatible data solutions (e.g., using Docker, Kubernetes, AWS, Azure)\nContribute to end-to-end solution integration with dashboards, workflow automation platforms, digital twin systems, AI copilots, or decision-support applications\nDevelop reusable APIs, services, and automation components that enable scalable deployment of analytics, Gen-AI, and workflow solutions across research and industry projects\n\nJob Requirements\nBachelor’s/Master’sdegree in Computer Science, Data Engineering, Information Systems, or a related field\nStrong proficiency in Python and SQL; familiarity with PySpark, Pandas, or Dask is a plus\nProven experience building data pipelines in cloud or hybrid environments\nRelevant hands-on experience in Gen-AI solution enablement, including RAG pipelines, vector databases, semantic search, knowledge-base preparation, prompt engineering support, or LLM application integration\nExperience designing or implementing workflow automation solutions using APIs, orchestration tools, low-code/no-code platforms, robotic process automation, or enterprise automation frameworks\nFamiliarity with supply chain data systems (SAP, ERP, MES) and industry-specific data schemas is highly preferred\nHands-on experience with data lakes, data warehousing, or streaming architectures\nExcellent interpersonal and communication skills; ability to work in cross-functional R&D teams\nBonus: Familiarity with supply chain KPIs, AI/ML workflows, Gen-AI application patterns, agentic workflows, and applied industrial analytics is advantageous","description_format":"text","description_chars":3859,"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":[],"hiring_excludes":[],"relocation_offered":false,"industries":[],"lifecycle":[{"event":"open","at":"2026-09-25T18:38:00Z"}],"liveness":{"score":89,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.894,"p_room":1,"age_days":5,"expected_fill_days":31,"reasons":["conf:8","velocity","win:early","comp:brand"],"computed_at":"2026-10-01T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/astar-leadsenior-research-engineer","json_url":"https://alion.io/job/astar-leadsenior-research-engineer.json","meta":{"generated_at":"2026-10-02T02:44:39Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","usage":{"tier":"crawler","counted_by":"address","units_charged":1,"used_today":3709,"day_limit":5000,"remaining_today":1291,"minute_limit":60,"resets_at":"2026-10-03T00:00:00Z"}}}