Remote Biomedical Ontology & Data Standards Expert

Mind Moves

United States

Remote

USD 179,000 - 220,000

Part time

14 days+

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Job summary

Mind Moves is seeking a Biomedical Subject Matter Expert to provide senior scientific guidance on evaluation framework development, benchmark dataset validation, and scientific review for the NLM Biomedical AI Challenges.

The role centers on mapping dbGaP variables to standard vocabularies (LOINC, UMLS, CUIs), curating semantic annotations, and ensuring interoperability via the dbGaP FHIR API and related data dictionaries. Remote within the US preferred.

Qualifications

Advanced degree (MS or PhD preferred) in bioinformatics, biomedical informatics, epidemiology, genetics, library/information science, or a related field — or equivalent professional experience.
Demonstrated experience with biomedical controlled vocabularies and terminology standards (e.g., UMLS, LOINC, MeSH, PhenX, or OBO Foundry ontologies such as HPO or OBI).
Familiarity with clinical/genomic data repositories and data-sharing models comparable to dbGaP (e.g., NIH CDE Repository, TOPMed, other GWAS/genomic consortia).
Experience with semantic web or ontology tooling (e.g., OWL, RDF, Protégé).
Experience with Python or R.
Strong written communication skills for documenting mapping rationale.
Working knowledge of information-retrieval and classification evaluation metrics used to grade automated systems (precision, recall, F1-score, NDCG) and how to translate expert judgment into a defensible ground-truth/reference dataset.
Preferred Qualifications: Prior experience with dbGaP data dictionaries, submission packets, or the dbGaP FHIR API.
Experience with data-dictionary harmonization tools (e.g., D2Refine or similar).
Familiarity with GWAS/genomic consortium data harmonization processes.
Experience with FHIR resource modeling in a research-data or genomics context.
Prior experience building or contributing to benchmark/gold-standard datasets for an evaluation campaign or shared task (e.g., TREC-style relevance judgments, NLP/IR bake-offs) and calibrating multiple annotators to a shared rubric.

Responsibilities

Map dbGaP variables to controlled vocabularies and common data elements, including PhenX measurement protocols, using the dbGaP data dictionary's VARIABLE_SOURCE and SOURCE_VARIABLE_ID fields, and classify mappings by confidence level (e.g., identical, comparable, or related) consistent with dbGaP/PhenX conventions.
Curate and validate semantic annotations linking dbGaP variables to standard terminologies such as the UMLS Metathesaurus, LOINC, and UMLS Concept Unique Identifiers (CUIs), following the approach used by groups like NLM's Lister Hill Center, Medical Data Models, and NHLBI's TOPMed program.
Support FHIR-based interoperability by ensuring annotated vocabularies are correctly represented in the dbGaP FHIR schema and accessible via the dbGaP FHIR API.
Design and curate the expert-adjudicated reference ("gold standard") mapping set against which Track 1 participant submissions are scored.
Author and maintain annotation guidelines and adjudication protocols so reference-set decisions are reproducible and defensible under review or challenge.
Run or oversee inter-annotator agreement checks across SME reviewers and resolve disagreements before a mapping enters the reference set.
Advise on Track 2 relevance judgments — labeling dbGaP studies as relevant/not relevant to natural-language research queries.
Contribute to held-out or adversarial "probe" cases used to detect memorized or hardcoded submissions during anti-gaming review.