Data Harmonization Analyst
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We have an exciting opportunity to join our team as a 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 resulting 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 support implementation of integrated standards []tailored for NAMs data. This position focuses on analyzing source data structures, developing source-to-target mapping specifications, authoring ETL functional requirements and data quality assurance frameworks, and ensuring that harmonization workflows are well-defined, reproducible, and aligned with FAIR data principles. The role is analytical and specification-oriented: the Data Analyst designs and documents the logic that guides implementation, rather than executing engineering tasks directly.
Job Responsibilities:
Analyze source NAMs datasets (such as transcriptomics, proteomics, microscopy, imaging, electrophysiology, etc.) to characterize data structure, content, and quality prior to harmonizationDevelop detailed source-to-CDM mapping specifications, including transformation rules, value set crosswalks, and handling of edge casesAuthor functional ETL requirements and data flow documentation to guide pipeline development by engineering staffDesign data quality assurance (QA) frameworks and acceptance criteria for NAMs datasets, including completeness, conformance, and plausibility checksEvaluate and document terminology alignment across existing Vocabularies, Metadata requirements, and source ontologiesConduct mapping gap analyses and propose remediation strategies for non-standard or missing terminology coverageCollaborate with the Lead Metadata and Standards Specialist to ensure mapping outputs align with metadata standardsProduce and maintain clear analytical documentation: data dictionaries, mapping catalogs, QA specification sheets, and implementation guideSupport onboarding of new data contributors by reviewing their data structures and advising on harmonization pathwaysParticipate in data quality review cycles, analyze QA outputs, and document findings and recommended remediation stepsMinimum 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 3 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.)
Good grasp of soware engineering principles. Experience in integrating modern
software architectures.
Knowledge and some experience in operational aspects of soware development and
deployment, including automation, testing, virtualization and container technology
Knowledge of clinical and operational aspects of healthcare delivery.
Excellent written and oral communication skills for a variety of audiences
Preferred Qualifications:
Experience with OMOP Common Data Model or other biomedical research CDMs
Experience with programming languages (Python, JAVA, R)
Familiarity with healthcare or life sciences data standards (UMLS, SNOMED-CT, LOINC), or sequencing data standards (FASTQ, BAM, VCF), etc.
Knowledge of FAIR data principles and metadata standards
Familiarity with NAMs methodologies or preclinical research data
Experience with federated data networks or distributed query systems
Familiarity with AI/ML tools applied to terminology matching, automated mapping recommendations, or data quality assessment
Qualified candidates must be able to effectively communicate with all levels of the organization.
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