Data Analytics Analyst II

Endeavor Health•Skokie, IL

1d•Hybrid

About The Position

The Neaman Center for Personalized Medicine seeks an experienced Data Analytics Analyst to support clinical, operational, and research initiatives across Endeavor Health's precision medicine program. This program has supported genetic testing for over 50,000 patients through a clinical population genetic screening program. Endeavor Health is a genomic learning health system. This position serves as a key analytics partner to clinicians, genetic counselors, pharmacists, informaticists, researchers, and operational leaders, translating complex clinical and business questions into rigorous, actionable analytics. The Data Analytics Analyst will work across a broad range of clinical and genomic data sources, including Epic Clarity and enterprise data warehouse data, to develop and maintain analytic datasets, clinical measures, reports, dashboards, and other data products. The position requires strong SQL and data analysis skills, an ability to understand complex clinical workflows and data structures, and the judgment to assess data quality and communicate limitations clearly to clinical and operational stakeholders. This is a highly collaborative role for an individual who enjoys solving ambiguous problems, working directly with subject-matter experts, and building analytic solutions that support the delivery, evaluation, and improvement of personalized medicine. The analyst serves as the dedicated analytics resource for the precision medicine program, while also participating in a broader research scrum team alongside other analysts working on separate initiatives.

Requirements

Bachelor's degree in computer science, data science, statistics, mathematics, health informatics, healthcare administration, or a related field.
5+ years of experience in healthcare analytics, clinical data analysis, business intelligence, health informatics, or a related field.
Strong proficiency in SQL and experience working with relational healthcare data.
Experience working with electronic health record data, preferably Epic/Clarity.
Experience with Python, R, SAS, or another programming language used for data analysis and/or data engineering.
Demonstrated ability to work with complex, imperfect, and heterogeneous healthcare data.
Strong analytical and problem-solving skills, including the ability to translate ambiguous questions into well-defined analytic approaches.
Demonstrated ability to communicate technical concepts and analytic findings effectively to clinical and nontechnical audiences.
Experience collaborating directly with clinicians, researchers, or other healthcare subject-matter experts.
Strong organizational skills and ability to manage multiple projects and competing priorities.
Programming & Statistical Analysis: Python, with familiarity in Pandas, SciPy, and Statsmodels. Statistical modeling and hypothesis testing. Quantitative research methods.
SQL: All join types, aggregation, and set operations. DDL/DML. Correlated and non-correlated subqueries. Window functions and common table expressions. Query optimization.
Clinical Data Systems: Epic Clarity and the Clinical Data Model. Experience integrating multiple internal and external data sources. Familiarity with external/public data sources such as CDC and NLM.
Environment & Tooling: Git/GitHub, Linux, PuTTY/WinSCP or similar remote access/file transfer tools.
Reporting & Visualization: Power BI and/or Tableau.
Healthcare Content Knowledge: Healthcare workflows, particularly orders and encounters. ICD-10/ICD-9, CPT/HCPCS coding, and claims data. Basic genetics.

Nice To Haves

IHI (preferred)
Epic Cogito (preferred)
Graduate degree in computer science, statistics, data science, health informatics, or a related field.
Experience with precision medicine, genomics, pharmacogenomics, oncology, or other clinical specialty data.
Experience developing clinical quality measures, patient cohorts, or population-health measures.
Experience with clinical terminology, medication data, diagnosis data, or other standardized healthcare vocabularies.
Experience developing data pipelines or other production-oriented analytic workflows.
Experience with Tableau or other business intelligence and data-visualization platforms.
Experience conducting clinical data validation.
Experience supporting clinical research or quality-improvement initiatives.
Familiarity with clinical workflows and healthcare data architecture.

Responsibilities

Partner with clinicians, genetic counselors, pharmacists, researchers, and operational leaders to identify analytic needs and translate clinical and operational questions into measurable outcomes and analytic requirements.
Develop, maintain, and optimize complex SQL-based queries and analytic datasets using Epic Clarity and other enterprise data sources.
Develop Python-based data pipelines and analytic workflows to integrate, transform, validate, and analyze clinical, genomic, medication, and operational data.
Design and maintain clinical measures, performance indicators, reports, dashboards, and other data products used to monitor personalized medicine program operations and outcomes, including patient-journey tracking (e.g., consent, assessment completion, testing turnaround, result-return timing).
Develop analytic definitions and patient-identification algorithms for clinical populations, procedures, medications, diagnoses, genetic testing, and other programmatic measures.
Evaluate the completeness, accuracy, consistency, and clinical validity of data across multiple sources; identify discrepancies and develop strategies to reconcile or supplement data.
Collaborate with clinicians and other subject-matter experts to establish clinically appropriate definitions, validate analytic logic, and interpret results.
Clearly communicate data limitations, assumptions, methodology, and analytic findings to audiences with varying levels of technical expertise.
Develop documentation of data definitions, analytic methodologies, source-system characteristics, and known limitations to support reproducibility and appropriate interpretation of results.
Support research and quality-improvement initiatives through data extraction, cohort development, statistical analysis, and analytic consultation.
Identify opportunities to improve the availability and quality of clinical data, including collaboration with clinical informatics and data-management stakeholders on standardized data definitions and terminology.
Monitor and troubleshoot existing analytic processes and proactively identify opportunities to improve efficiency, scalability, data quality, and maintainability.
Manage multiple concurrent analytic projects, balancing stakeholder priorities, organizational objectives, project scope, data availability, and timelines.
Present analytic findings and recommendations to clinical, operational, and research audiences using clear, audience-appropriate visualizations and presentations.
Contribute to a collaborative analytics environment through knowledge sharing, peer consultation, documentation, and mentorship.