Senior Metadata and Standards Specialist

Position Summary Senior Data Science Analyst/Engineer – working as part of the Complement‑ARIE program to develop a controlled access platform for researchers that standardizes and harmonizes NAMs data, stores it securely, and provides advanced analytical and visualization tools. The successful candidate will co‑lead the design and implementation of a comprehensive metadata framework ensuring FAIR compliance and data discoverability across the NAMs Data Hub. Job Responsibilities Metadata Framework Design and Governance Design and implement FAIR and interoperable metadata framework for NAMs data Define and maintain metadata schemas, profiles, and validation rules for NAMs data modalities such as clinical, omics, imaging, experimental and observational data Integrate provenance models (e.g., PROV‑O) into the metadata framework Develop comprehensive metadata dictionaries Oversee metadata versioning, change management, and deprecation processes, ensuring release notes, impact assessments, and backward compatibility Establish metadata quality assessment frameworks Ensure metadata standards and implementations align with institutional and external policies, including privacy, access control, and data use conditions Ontologies, Terminologies, and Semantic Modeling Select and integrate appropriate reference terminologies and ontologies for NAMs data representation into a comprehensive NAMs ontology framework Perform gap analysis and define mappings and crosswalks between local terminologies and reference ontologies Design semantic models using RDF/OWL, SKOS, JSON‑LD, and related standards to enable machine‑interpretable, ontology‑driven data integration and reasoning Integrate NAMs terminologies and ontologies with existing Standardized Vocabularies and align Metadata schemas with an evolving CDM Metadata and Ontology Workflows Collaborate with data engineering and analytics teams to integrate metadata and ontology services into NAMs Hub pipelines and analytical platforms Participate in the adoption of emerging AI‑enabled tools for metadata extraction, enrichment, and quality assurance Use AI‑assisted coding tools to accelerate development of schemas, validation scripts, and queries Collaboration and Community Engagement Develop and deliver training and guidance materials on metadata, ontologies, and FAIR data for internal teams and external collaborators Coordinate multi‑institutional metadata and standards activities, including consensus‑building, review cycles, and formal approval processes Participate in the Standardization Workgroup to develop and refine standards Collaborate with data standard organizations such as CFDE and OHDSI to align NAMs metadata standards with their data models and standardized vocabularies Minimum Qualifications Master’s degree in a quantitative discipline (Biomedical Informatics, Computer Science, Machine Learning, Applied Statistics, Mathematics or similar field) 5‑7 years of experience in machine learning/data science Proficiency in at least one programming language (Python, R) and machine learning tools (scikit‑learn, R) Knowledge of predictive modeling and machine learning concepts, including design, development, evaluation, deployment and scaling to large datasets Familiarity with computing models for big data (Hadoop/MapReduce, Spark, etc.) Knowledge of relational/SQL, NoSQL (MongoDB, etc.) databases Preferred Qualifications PhD degree Demonstrated track record of successfully applying metadata and data standards to research or operational datasets, with tangible outcomes such as improved interoperability, FAIR assessments, or adoption by external stakeholders Deep knowledge of FAIR principles and their practical application Strong understanding of metadata standards including Dublin Core, DataCite, DCAT, PROV‑O, and Schema.org Experience with RDF, SKOS, SPARQL, and semantic web standards for metadata representation Knowledge of controlled vocabularies, taxonomies, and ontologies for metadata annotation Experience with schema definition languages (e.g., LinkML) and validation frameworks Knowledge of biomedical terminologies and ontologies Strong analytical skills for metadata modeling and information architecture Excellent documentation skills and ability to create clear technical specifications and guidelines Strong communication skills for training and stakeholder engagement Experience working collaboratively in multi‑institutional research environments Experience with version control systems for managing schemas and documentation Experience with Common Fund Data Ecosystem (CFDE) and the C2M2 metadata model Understanding of biomedical data types including genomics, imaging, and laboratory data Knowledge of OMOP CDM and standardized vocabularies Published work on metadata standards or FAIR data implementation Experience coordinating metadata and data standardization initiatives across multiple institutions Experience with automated metadata extraction and enrichment tools Familiarity with AI/ML tools and methods, including their application to metadata enrichment, automated annotation, or standards development workflows Familiarity with NAMs methodologies and alternative testing approaches Qualified candidates must be able to effectively communicate with all levels of the organization. The role offers a competitive salary range of $121,792.22 – $162,052.80 annually and a comprehensive benefits and wellness program supporting health, financial, and work‑life balance. NYU Grossman School of Medicine is an equal opportunity employer and committed to inclusion in all aspects of recruiting and employment. All qualified individuals are encouraged to apply and will receive consideration. NYU Langone Health provides a salary range to comply with the New York state Law on Salary Transparency in Job Advertisements. The salary range for the role is $121,792.22 – $162,052.80 annually. Actual salaries depend on a variety of factors, including experience, specialty, education, and hospital need. The salary range or contractual rate listed does not include bonuses, incentive, differential pay or other forms of compensation or benefits. #J-18808-Ljbffr NYU Grossman School of Medicine