Data Analyst II - Medical Informaticist

NYU Langone Health • New York, NY

16d • $70,482 - $106,180

About The Position

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.

Requirements

• Bachelors degree in Biomedical Informatics, Health Informatics, Data Science, Biostatistics, Computer Science, Public Health, Epidemiology, or related field required
• 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.

Nice To Haves

• Masters degree preferred.
• Experience working with OMOP Common Data Model, OHDSI tools or methods, or other common clinical research data models is strongly preferred.
• 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.

Responsibilities

• 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.
• 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.
• 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.
• 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.
• 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.

Benefits

• financial security benefits
• generous time-off program
• employee resources groups for peer support
• holistic employee wellness program (physical, mental, nutritional, sleep, social, financial, and preventive care)

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level