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Metro location and mid-level seniority increase competition, but niche KR/LLM specialization reduces applicant pool.
Strong ML/KR technical skills transfer widely, though healthcare supply-chain domain knowledge increases domain specificity.
Explicit '>4 years' requirement plus mandatory KR, LLM, RDF/SPARQL and ML production skills make screening strict.
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Design, build, and maintain knowledge representation solutions such as entity resolution systems, embedding-based semantic similarity, and LLM-assisted knowledge extraction pipelines.
Develop frameworks for uncertainty quantification and confidence scoring in knowledge representation outputs to ensure reliability for downstream consumers.
Collaborate with data quality engineers and stakeholders to improve KR pipelines and stay current with AI/ML advances relevant to healthcare supply chain data.
More than 4 years of applied machine learning and data science experience with direct work in entity resolution, semantic matching, or knowledge representation.
Hands-on experience with LLM-assisted knowledge extraction including prompt design, structured parsing, and domain adaptation.
Working knowledge of RDF and SPARQL, with strong programming skills in ML tools like PyTorch, HuggingFace, scikit-learn, and graph data tooling (e.g., RDFLib, NetworkX).
Work Experience Required: >4 years in relevant applied ML/data science fields.
Proficient in bridging statistical ML outputs with formal knowledge representation frameworks such as OWL and description logics, enabling reasoning about ontological assertions.
Experienced operating in multi-disciplinary environments where ML supports formal systems or decision-making processes, particularly within healthcare supply chain domains.
Technically skilled in producing scalable, production-ready knowledge graph and LLM-based solutions with an emphasis on interpretability and evaluation.