Senior Metadata and Standards Specialist

NYU Langone Health • New York, NY

7d • $121,792 - $162,053

About The Position

NYU Grossman School of Medicine is a top-ranked medical school with a 175-year history of training physicians and scientists. As part of NYU Langone Health, it is committed to improving the human condition through education, research, and patient care. Equity and inclusion are fundamental values, fostering an environment where diverse talent can thrive. This position is part of the Complement-ARIE program, establishing the NYU-Sage New Approach Methodologies (NAMs) Data Hub and Coordinating Center. This hub will create a controlled access platform for researchers to share and analyze data from NAMs approaches, developing tools for standardization, harmonization, secure storage, and powerful analytical and visualization capabilities. The Senior Data Science Analyst/Engineer will co-lead the design and implementation of a comprehensive metadata framework to ensure FAIR compliance and data discoverability. This role involves extending the C2M2 model, developing Common Data Elements (CDEs), integrating terminologies and ontologies, embedding provenance tracking, and authoring metadata schemas with validation rules for interoperability.

Requirements

• Masters degree in a quantitative discipline (Biomedical Informatics, Computer Science, Machine Learning, Applied Stascs, 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 (scikitlearn, 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 databases (Relational / SQL, NOSQL MongoDB etc.)
• Ability to effectively communicate with all levels of the organization.

Nice To Haves

• PhD degree
• Demonstrated track record of successfully applying metadata and data standards to research or operational datasets in any biomedical field, with tangible outcomes such as improved interoperability, FAIRness 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 and practical experience with biomedical terminologies and ontologies
• Strong analytical skills for metadata modeling and information architecture
• Excellent documentation skills with ability to create clear technical specifications and guidelines
• Strong communication skills for training and stakeholder engagement
• Demonstrated ability to work 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

Responsibilities

• 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, changemanagement, and deprecation processes, ensuring release notes, impact assessments, and backwardcompatibility
• Establish metadata quality assessment frameworks
• Ensure metadata standards and implementations align with institutional and external policies including privacy, access control, and data use conditions
• 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
• In collaboration with the engineering team, integrate NAMs terminologies and ontologies with existing Standardized Vocabularies and align Metadata schemas with an evolving CDM
• 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
• 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 related data standard organizations such as CFDE and OHDSI to align NAMs metadata standards with their data models and standardized vocabularies.

Benefits

• Financial security benefits
• Generous time-off program
• Employee resources groups for peer support
• Holistic employee wellness program (physical, mental, nutritional, sleep, social, financial, and preventive care)

What This Job Offers

• Job Type: Full-time
• Career Level: Senior