Data Analyst II Medical Informaticist

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, 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 tomed.nyu.edu, and interact with us on LinkedIn, Glassdoor, Indeed, Facebook, TwitterandInstagram.Position Summary:We have an exciting opportunity to join our team as a Analyst II - DataCore. NYU Langone Health seeks a Data Analyst - Medical Informaticist to support the design, curation, harmonization, and analysis of clinical data assets used for observational research, cohort discovery, phenotyping, and evidence generation across the enterprise. This role sits within the Medical Center Information Technology (MCIT) and is intended for candidates with strong healthcare data management, clinical informatics, and analytic programming skills who can help transform complex EHR and research data into research-ready datasets, validated cohorts, and reproducible evidence workflows.The position is designed for analysts with deep familiarity with Epic and related EHR data ecosystems, OMOP Common Data Model environments, clinical data warehouses, tumor registry data, clinical notes, and standard biomedical vocabularies. The ideal candidate combines strong data engineering and analytic capability in Python, R, SQL, and related tools with practical experience in ontology-based harmonization, metadata management, data quality assessment, and cohort-based observational studies using heterogeneous real-world data sources.This role also values candidates who can work effectively in modern AI-enabled analytic environments. Experience using AI coding assistants and development tools such as GitHub Copilot, OpenAI Codex, Claude Code, or related platforms to accelerate analytic programming, ETL development, documentation, code review, and quality assurance is highly relevant to success in this position.Job Responsibilities:Clinical Data Curation and Real-World Evidence SupportBuild and maintain research-ready clinical datasets for observational studies, cohort characterization, computable phenotyping, and real-world evidence generation.Extract, transform, and curate data from Epic and related institutional data sources, including structured EHR data, tumor registry assets, clinical documentation, laboratory data, medication data, pathology, imaging-linked metadata, and other relevant clinical systems.Support cohort identification, patient screening, longitudinal outcome tracking, and registry-style data assembly for clinical and translational research use cases.Develop reproducible workflows for data extraction, transformation, curation, and handoff to downstream analytics, reporting, and data science teams.OMOP, Standards, and Ontology-Based HarmonizationMap institutional source data to standard terminologies and analytic data models including OMOP Common Data Model and related OHDSI ecosystem approaches.Support terminology mapping and ontology-based harmonization using vocabularies such as SNOMED CT, ICD-10-CM, LOINC, CPT, RxNorm, HGNC, HPO, and related clinical and biomedical standards.Develop and maintain ETL logic, concept mapping workflows, metadata definitions, source-to-target specifications, and semantic crosswalks needed for interoperable analytics.Contribute to metadata management, lineage tracking, provenance documentation, and harmonization practices that improve reproducibility, tra