Job 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 , 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 , and interact with us on LinkedIn , Glassdoor , Indeed , Facebook , Twitter and Instagram . 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 Support Build 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 Harmonization Map 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, transparency, and secondary use of clinical data. Phenotyping, Cohort Logic, and Outcomes Data Translate clinical and research concepts into computable phenotype definitions, cohort rules, variable definitions, and executable data queries. Support development of phenotype and outcome datasets using structured EHR data, registry information, and clinical text-derived variables when appropriate. Curate gold-standard outcomes and other validation datasets for research, quality assessment, and analytic model support. Assist in cohort characterization, subgroup definition, temporal event sequencing, and longitudinal data assembly for observational analyses. Data Quality, Validation, and Research Readiness Perform data quality assessment across source and curated datasets, including completeness, plausibility, duplication, temporal consistency, semantic accuracy, and conformance to expected standards. Implement validation checks and quality assurance workflows that identify anomalies, mapping issues, transformation errors, and inconsistencies that could affect research validity. Conduct chart review support, targeted validation studies, and discrepancy analysis for phenotype definitions, outcomes, and derived variables. Document transformation logic, assumptions, and validation results to support auditability, reproducibility, and compliant research operations. Collaboration and Enablement Collaborate with MCIT engineers, clinical informaticians, analysts, researchers, data scientists, and operational teams to define requirements and deliver fit-for-purpose analytic datasets. Support observational studies, registry analyses, comparative effectiveness workflows, and precision medicine or trial-support use cases by providing curated data, cohort logic, and analytic context. Contribute to dashboards, cohort reports, operational tracking outputs, and analyst-facing documentation that improve data usability across teams. Apply efficient coding and workflow practices, including appropriate use of AI coding tools, to improve productivity, maintainability, and analytic quality. Minimum Qualifications: To qualify you must have a Education Bachelors degree in Biomedical Informatics, Health Informatics, Data Science, Biostatistics, Computer Science, Public Health, Epidemiology, or related field required; Masters degree preferred. Experience 3+ years of relevant experience working with clinical, observational, or research data in a health system, academic medical center, payer, life sciences, or related healthcare environment. Demonstrated experience with Epic or comparable EHR data ecosystems, clinical data warehouses, and research-oriented data extraction and curation. Experience working with OMOP Common Data Model, OHDSI tools or methods, or other common clinical research data models is strongly preferred. Experience supporting cohort definition/identification, phenotyping, registry-style datasets, chart review workflows, data quality assessment, or observational analytics. Experience using Python, R, SQL, and related analytic tools for healthcare data management and reproducible analysis. 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 . 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 $70,481.61 - $106,180.20 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

Requirements

Must-haves

For more information, go to med.nyu.edu , and interact with us on LinkedIn , Glassdoor , Indeed , Facebook , Twitter and Instagram .
We have an exciting opportunity to join our team as a Analyst II - DataCore.
Clinical Data Curation and Real-World Evidence Support
Build and maintain research-ready clinical datasets for observational studies, cohort characterization, computable phenotyping, and real-world evidence generation.
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 Harmonization
Map 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, transparency, and secondary use of clinical data.
Phenotyping, Cohort Logic, and Outcomes Data
Translate clinical and research concepts into computable phenotype definitions, cohort rules, variable definitions, and executable data queries.
Support development of phenotype and outcome datasets using structured EHR data, registry information, and clinical text-derived variables when appropriate.
Curate gold-standard outcomes and other validation datasets for research, quality assessment, and analytic model support.
Assist in cohort characterization, subgroup definition, temporal event sequencing, and longitudinal data assembly for observational analyses.
Data Quality, Validation, and Research Readiness
Perform data quality assessment across source and curated datasets, including completeness, plausibility, duplication, temporal consistency, semantic accuracy, and conformance to expected standards.
Implement validation checks and quality assurance workflows that identify anomalies, mapping issues, transformation errors, and inconsistencies that could affect research validity.
Conduct chart review support, targeted validation studies, and discrepancy analysis for phenotype definitions, outcomes, and derived variables.
Document transformation logic, assumptions, and validation results to support auditability, reproducibility, and compliant research operations.
Collaboration and Enablement

Nice-to-haves

Bachelors degree in Biomedical Informatics, Health Informatics, Data Science, Biostatistics, Computer Science, Public Health, Epidemiology, or related field required; Masters degree preferred.
Experience working with OMOP Common Data Model, OHDSI tools or methods, or other common clinical research data models is strongly preferred.

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