Data Harmonization Analyst
$84,578–$126,992 year
On-siteNew York, United States
Job Summary
Analyze source NAMs datasets to characterize structure, content, and quality prior to harmonization. Develop detailed source-to-CDM mapping specifications, including transformation rules, value set crosswalks, and edge case handling. Author functional ETL requirements and data flow documentation to guide pipeline development by engineering staff. Design data quality assurance frameworks and acceptance criteria for NAMs datasets, including completeness, conformance, and plausibility checks. Evaluate and document terminology alignment across existing vocabularies, metadata requirements, and source ontologies. Conduct mapping gap analyses and propose remediation strategies for non-standard or missing terminology coverage. Collaborate with the Lead Metadata and Standards Specialist to ensure mapping outputs align with metadata standards. Produce and maintain clear analytical documentation: data dictionaries, mapping catalogs, QA specification sheets, and implementation guides. Support onboarding of new data contributors by reviewing their data structures and advising on harmonization pathways. Participate in data quality review cycles, analyze QA outputs, and document findings and recommended remediation steps.
Required Qualifications
- Masters degree in a quantitative discipline (Biomedical Informatics, Computer Science, Machine Learning, Applied Stascs, Mathematics or similar field)
- 3 years of experience in machine learning/ data science
- Proficiency in at least one programming language (Python, R)
- 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
- Must be able to effectively communicate with all levels of the organization
Desired 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
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