Senior Metadata and Standards Specialist at NYU Langone Health

New York, New York, United States -

Full Time

Start Date

Immediate

Expiry Date

13 Aug, 26

Salary

162052.8

Posted On

15 May, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Metadata Framework Design, FAIR Principles, Ontology Modeling, RDF/OWL, Python, R, SQL, Semantic Web Standards, Biomedical Informatics, Data Governance, Machine Learning, Schema Validation, SPARQL, JSON-LD, Information Architecture, Data Harmonization

Industry

Hospitals and Health Care

Description

NYU Grossman School of Medicine is one of the nation's top-ranked medical schools. For 175 years, NYU Grossman School of Medicine has trained thousands of physicians and scientists who have helped to shape the course of medical history and enrich the lives of countless people. An integral part of NYU Langone Health [http://nyulangone.org], the Grossman School of Medicine at its core is committed to improving the human condition through medical education, scientific research, and direct patient care. At NYU Langone Health, equity and inclusion are fundamental values. We strive to be a place where our exceptionally talented faculty, staff, and students of all identities can thrive. We embrace inclusion and individual skills, ideas, and knowledge. For more information, go to med.nyu.edu [http://med.nyu.edu/], and interact with us on LinkedIn [https://www.linkedin.com/company/nyulangonehealth/?viewAsMember=true], Glassdoor [https://www.glassdoor.com/Reviews/NYU-Langone-Health-Reviews-E121521.htm], Indeed [https://www.indeed.com/cmp/Nyu-Langone-Health/reviews], Facebook [https://facebook.com/NYUSchoolofMedicine], Twitter [https://twitter.com/nyuschoolofmed] and Instagram [https://instagram.com/nyuschoolofmed]. Position Summary: We have an exciting opportunity to join our team as a Senior Data Science Analyst/Engineer. As part of the Complement-ARIE program, the NYU-Sage New Approach Methodologies (NAMs) Data Hub and Coordinating Center will create a controlled access platform for researchers to share and analyze data from NAMs approaches. The program will build tools to standardize and harmonize NAMs data, store it securely, and provide researchers with powerful analytical and visualization tools. The successful candidate will co-lead the design and implementation of a comprehensive metadata framework ensuring FAIR (Findable, Accessible, Interoperable, Reusable) compliance and data discoverability across the NAMs Data Hub. This position will extend the C2M2 (Crosscut Metadata Model) to create an integrated framework for NAMs data, develop Common Data Elements (CDEs), integrate terminologies and ontologies for NAMs representation, embed provenance tracking, and author and maintain metadata schemas defining data structures and validation rules to ensure interoperability across all NAMs datasets. 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, 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 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 * In collaboration with the engineering team, 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 related data standard organizations such as CFDE and OHDSI to align NAMs metadata standards with their data models and standardized vocabularies. Minimum Qualifications: To qualify you must have a Masters degree in a quantitative discipline (Biomedical Informatics, Computer Science, Machine Learning, Applied Stascs, Mathematics or similar field) and 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.) Preferred Qualifications: 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 Qualified candidates must be able to effectively communicate with all levels of the organization. NYU Grossman School of Medicine provides its staff with far more than just a place to work. Rather, we are an institution you can be proud of, an institution where you'll feel good about devoting your time and your talents. At NYU Langone Health, we are committed to supporting our workforce and their loved ones with a comprehensive benefits and wellness package. Our offerings provide a robust support system for any stage of life, whether it's developing your career, starting a family, or saving for retirement. The support employees receive goes beyond a standard benefit offering, where employees have access to financial security benefits, a generous time-off program and employee resources groups for peer support. Additionally, all employees have access to our holistic employee wellness program, which focuses on seven key areas of well-being: physical, mental, nutritional, sleep, social, financial, and preventive care. The benefits and wellness package is designed to allow you to focus on what truly matters. Join us and experience the extensive resources and services designed to enhance your overall quality of life for you and your family. 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. We require applications to be completed online. View Know Your Rights: Workplace discrimination is illegal [https://www.eeoc.gov/sites/default/files/2023-06/22-088_EEOC_KnowYourRights6.12ScreenRdr.pdf]. 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. To view the Pay Transparency Notice, please click here [https://cdn-static.findly.com/wp-content/uploads/sites/290/2017/04/pay-transp_formattedESQA508c.pdf]

Responsibilities

Co-lead the design and implementation of a comprehensive metadata framework to ensure FAIR compliance for NAMs data. Develop metadata schemas, integrate reference ontologies, and collaborate with multi-institutional partners to standardize biomedical data.