Senior Data Scientist

GHX India; Hyderabad; On-site; INR 4,000,000 - 9,000,000; Full time

Job summary

GHX India seeks a Senior Data Scientist – Knowledge Representation to advance knowledge graphs, entity resolution, and LLM-assisted extraction on the HCSC KR platform.

The role focuses on building scalable KR solutions across graphs, documents, and semi-structured data, balancing performance and cost while collaborating with cross-functional teams.

Qualifications

• Greater than 4 years of applied ML and data science experience with entity resolution or knowledge graphs.
• Hands-on experience with LLM-assisted knowledge extraction and domain adaptation.
• Working knowledge of RDF and SPARQL.
• Strong programming skills for ML pipelines (PyTorch, HuggingFace, scikit-learn) and graph tooling.

Responsibilities

• Design and implement knowledge representation solutions balancing performance and costs across multiple data stores.
• Design, build, and maintain entity resolution systems.
• Develop embedding-based and hybrid semantic similarity solutions.
• Build and operate LLM-assisted knowledge extraction pipelines from HCSC data sources.
• Design uncertainty quantification frameworks for KR outputs and data quality signals.
• Collaborate with data quality engineers and stakeholders for feedback and alignment.

Skills

ML engineering

Knowledge representation

Embedding methods

LLM engineering

RDF/SPARQL

Graph technologies

Communication

Uncertainty quantification

Production models

Education

CS/Statistics/Computational Linguistics

Tools

RDFLib

NetworkX

PyTorch

HuggingFace

scikit-learn

Job description

Summary

The Senior Data Scientist – Knowledge Representation is a core technical role on the Healthcare Supply Chain (HCSC) Knowledge Representation (KR) team. This role brings machine learning, data science, and AI capabilities directly in service of knowledge representation problems: entity resolution, knowledge graphs, embedding-based semantic alignment, LLM-assisted knowledge extraction, and validation of ontological claims against data.

Job Title : Senior Data Scientist The Senior Data Scientist – Knowledge Representation is a core technical role on the Healthcare Supply Chain (HCSC) Knowledge Representation (KR) team. This role brings machine learning, data science, and AI capabilities directly in service of knowledge representation problems: entity resolution, knowledge graphs, embedding-based semantic alignment, LLM-assisted knowledge extraction, and validation of ontological claims against data.

Our platform connects hospitals, distributors, GPOs, manufacturers, and regulators, enabling transactional execution, clinical data alignment, and analytics optimization across organizational boundaries. Each party encodes its own implicit knowledge about the world in its schemas, identifiers, and workflows. The KR team's job is to make that implicit knowledge explicit, alignable, and trustworthy.

Essential Duties

• Design and implement knowledge representation solutions that balance performance and costs: using a variety of data stores (graph, document, tabular, vector).
• Design, build, and maintain entity resolution systems.
• Develop and own embedding-based and hybrid semantic similarity solutions.
• Build and operate LLM-assisted knowledge extraction pipelines that identify ontological candidates from unstructured and semi-structured HCSC data sources.
• Design and implement uncertainty quantification frameworks for KR outputs: confidence scoring for entity resolution decisions, calibration of alignment scores, and propagation of data quality signals through KR pipelines so that downstream consumers understand the reliability of what they receive.
• Partner with data quality engineers to establish bidirectional feedback channels between KR pipeline failures and systematic data quality issues.
• Collaborate with internal and external stakeholders.
• Stay current with developments in AI, ML and Data Science including knowledge graph embeddings, neuro-symbolic AI, and AI reasoning.

Competencies

• Strong ML engineering fundamentals: experience designing, training, evaluating, and operating models in production.
• Genuine engagement with knowledge representation concepts: sufficient familiarity with OWL, description logics, and ontological commitments to reason about what an ML model's outputs actually assert.
• Understanding and expertise in embedding methods for knowledge graphs and text with practical experience applying them to similarity, matching, and alignment tasks, and understanding of their failure modes in sparse or structurally heterogeneous domains.
• Practical LLM engineering skills: prompt engineering for structured extraction, fine-tuning or adapter methods for domain adaptation, retrieval-augmented generation for KR tasks, and rigorous evaluation of LLM outputs.
• Proficiency in knowledge graph technologies (RDF, SPARQL, property graphs) at a working level sufficient to query, evaluate, and annotate KR structures.
• Excellent cross-disciplinary communication: able to explain statistical model behavior and uncertainty to stakeholders.
• Comfort operating at the boundary of statistical and formal methods.
• Requires minimal supervision on ML and data science work within the KR domain.

Required Qualifications And Skills

• Greater than 4 years of applied ML and data science experience, with at least some portion of that work directly involving entity resolution, semantic matching, knowledge graph construction, or related knowledge representation problems.
• Hands‑on experience with LLM-assisted knowledge extraction: prompt design, structured output parsing, domain fine‑tuning or few‑shot adaptation.
• Working knowledge of RDF and SPARQL.
• Demonstrated ability to design and implement model evaluation frameworks.
• Strong programming skills for ML pipeline development, data analysis, and tooling; proficiency with the standard ML stack (e.g. PyTorch or equivalent, HuggingFace, scikit‑learn) and with graph data tooling (RDFLib, NetworkX, or equivalent).
• Experience working in multi‑disciplinary settings where ML outputs feed into formal systems or decision‑making processes.

Preferred Qualifications And Skills

• Bachelor's or advanced degree in Computer Science, Statistics, Computational Linguistics, Information Science, or a related discipline; graduate work in knowledge representation, NLP, or information extraction is a strong plus.
• Experience with neurosymbolic AI approaches: methods that combine learned representations with formal reasoning, constraint satisfaction, or logic‑based inference — particularly in the context of knowledge graph completion, ontology alignment, or structured prediction tasks.
• Healthcare supply chain domain knowledge.
• Familiarity with OWL 2 and description logics at a reading level: not required to author axioms independently, but able to read OWL and understand what a reasoner computes.
• Experience with graph database platforms at production scale.

GHX: It's the way you do business in healthcare

Global Healthcare Exchange (GHX) enables better patient care and billions in savings for the healthcare community by maximizing automation, efficiency and accuracy of business processes.

GHX is a healthcare business and data automation company, empowering healthcare organizations to enable better patient care and maximize industry savings using our world class cloud‑based supply chain technology exchange platform, solutions, analytics and services. We bring together healthcare providers and manufacturers and distributors in North America and Europe - who rely on smart, secure healthcare‑focused technology and comprehensive data to automate their business processes and make more informed decisions.

It is our passion and vision for a more operationally efficient healthcare supply chain, helping organizations reduce - not shift - the cost of doing business, paving the way to delivering patient care more effectively. Together we take more than a billion dollars out of the cost of delivering healthcare every year. GHX is privately owned, operates in the United States, Canada and Europe, and employs more than 1000 people worldwide. Our corporate headquarters is in Colorado, with additional offices in Europe.

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