{"id":2649067,"url":"https://alion.io/job/agency-for-science-technology-and-research-knowledge-graph-scientist","title":"Knowledge Graph Scientist","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":{"grade":"B","score":81,"open_postings":47,"ghost_share":0,"stale_share":0.979,"repost_share":0,"time_to_fill_p50_days":21,"computed_at":"2026-10-31T05:58:55Z"}},"role":"Science & Research","role_family":"Science & Research","seniority":"junior","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":{"min_usd":27000,"max_usd":61000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":29},"experience_years_min":2,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Fine-tuning","optional":false},{"name":"GraphRAG","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"LLM","optional":false},{"name":"Neo4j","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Digital Twin","optional":true},{"name":"Machine Learning","optional":true}],"status":"live","first_seen_at":"2026-10-15T21:06:35Z","employer_posted_date":"2026-10-15","last_verified_at":"2026-10-31T07:11:02Z","board_verified":true,"closed_at":null,"days_open":15,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":15},"description":"Job Description\nKnowledge Graph Scientist\n(Group: Sustainable Systems and Devices)\nAbout the role\nWe are seeking a motivated and research-driven Knowledge Graph Scientist to join our R&D team, focused on building the knowledge foundation for our remanufacturing platform. The role centres on designing ontologies, developing reasoning methods, and integrating large language model (LLM) technologies with knowledge graphs to support intelligent decision-making across the product lifecycle.\nYou will work closely with a multidisciplinary team of engineers, domain scientists, and industry partners to develop knowledge graphs in different domains such as EV battery, aerospace, and manufacturing remanufacturing contexts. This is a hands-on research role with expectations to publish, prototype, and lead the scientific direction of the KG programme.\nRoles and Responsibilities\nDesign and govern the core ontology covering parts, processes, regulations, and lifecycle states in EV battery, aerospace, and manufacturing domains, ensuring methodology scalability regardless of the industry domain\nDevelop and evaluate multi-hop reasoning methods, rule-based inference, and neuro-symbolic approaches that enable the system to derive new knowledge from existing graph facts\nResearch and prototype GraphRAG pipelines and LLM-to-KG extraction methods that automatically populate the graph from technical documents, inspection reports, and sensor logs\nDevelop cross-domain ontology alignment methods to unify terminology and schemas across data sources (e.g. engineering and supply chain), including handling conflicting industry standards\nResearch temporal knowledge graph structures that model how part conditions and states evolve across the remanufacturing lifecycle, enabling historical reasoning and predictive inference\nLead and contribute to research proposal development, technical roadmapping, and capability-building initiatives that align with industry needs and organisational strategy; proactively identify emerging technology opportunities and define new research directions for the KG programme.\nCollaborate with the KG Engineer to operationalise research prototypes into production-grade graph systems\nPrepare technical documentation, research reports, and presentations for internal and external stakeholders\nPublish findings at relevant research venues or domain-specific manufacturing and sustainability conferences\nJob Requirements\nTechnical Skills\nProficiency in Python, with experience in graph database systems (e.g., Neo4j, or Stardog)\nStrong foundation in knowledge representation and reasoning, including ontology design (OWL, RDFS, SKOS) and graph query languages (SPARQL, Cypher)\nDeep understanding of knowledge graph embedding methods (e.g., TransE, RotatE, ComplEx) and link prediction techniques\nExperience with LLM integration for knowledge tasks: Retrieval-Augmented Generation (RAG), information extraction, or fine-tuning language models on technical text\nFamiliarity with neuro-symbolic AI approaches combining neural learning with logic-based reasoning\nProficiency in one or more of the following areas:Temporal knowledge graphs and evolving graph structures\nMulti-hop reasoning and path-based inference\nNamed entity recognition and relation extraction from technical documents\nKnowledge graph completion and missing link prediction\n\nBonus Skills (Preferred but not required)\nFamiliarity with industrial or manufacturing data formats (BOMs, maintenance records, inspection schemas, ISO standards)\nExperience with digital twin frameworks or manufacturing informatics platforms\nQualifications\nPhD in Computer Science, Artificial Intelligence, or a related engineering field, with a focus on knowledge representation, semantic web, or graph-based machine learning\nPreferred: Demonstrated track record of independently securing competitive research grants, funding awards, or industry-sponsored projects.\nPreferred: At least 2 years of relevant post-doctoral or industry research experience, preferably with exposure to industrial or manufacturing knowledge domains","description_format":"text","description_chars":4092,"description_truncated":false,"requirements":{"experience_years_min":2,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"phd","optional":true},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Research 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