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# Analyst, IT, SR

- **Company:** Duke University
- **Location:** Durham, NC, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Business Analytics Applications, Data Analysis, Code Review, Data Validation, Data Cleansing, Data Security, Github, R (Programming Language), Python (Programming Language), Machine Learning, Software Engineering, SQL Databases, Systems Integration, Data Processing, Feature Engineering, Prompt Engineering, Electronic Medical Records, Gitlab, Microsoft Fabric, Information Technology, Text Analysis, Software Version Control, Data Pipelines
- **Published:** November 13, 2026
- **Apply:** https://careers.duke.edu/talentcommunity/apply/1450171900/?locale=en_US

## About the Role

* Bachelor's degree in computer science, data science, statistics, informatics, engineering, or a related field, or an equivalent combination of education and relevant experience, subject to Duke's approved job classification requirements.
 * Experience using Python, R and SQL for data preparation, analysis, or application development.
 * Experience applying statistical and machine learning methods, assessing model performance, and documenting reproducible analytical work.
 * Ability to work with multidisciplinary partners, translate requirements into technical work, and explain findings and limitations clearly.

Preferred Qualifications:

 * Experience with healthcare, EHR, clinical research, or quality improvement data.
 * Experience developing or evaluating large language model applications, including prompt design, retrieval-based approaches, or structured evaluation.
 * Experience with Microsoft Fabric, including lakehouses, notebooks, pipelines, or related data science workflows.
 * Familiarity with GitHub or GitLab for version control and collaboration
 * Familiarity with R is helpful but not required.
 * Experience with privacy-conscious data handling, research governance, data quality checks, and deployment or monitoring of analytic tools.

## Description

* Partner with clinical, research, quality, innovation, and operational teams to define project goals, analytic questions, success measures, deliverables, and feasible timelines.
 * Assess source systems alongside informaticists on EHR data definitions and extraction to build and validate analytic datasets.
 * Develop analytic plans that reflect the study population, data generating process, potential confounding or bias, and intended research or operational use.
 * Perform data exploration, descriptive and statistical analyses, feature engineering, and model development using reproducible code and appropriate validation methods.
 * Develop, adapt, and evaluate machine learning and large language model applications for approved use cases, including clinical text analysis. Assess accuracy, reliability, error patterns, subgroup performance, and limitations before recommending use.
 * Build and maintain reusable code, data pipelines, documentation, and analytic tools. Apply version control, code review, testing appropriate to risk, and change management practices.
 * Use Microsoft Fabric capabilities, including lakehouses and pipelines, where appropriate and available in approved Duke environments.
 * Create visualizations, reports, presentations, and interactive outputs that explain findings, uncertainty, and actionable implications to technical and nontechnical audiences.
 * Collaborate on implementation, monitoring, and maintenance plans for models or analytics used beyond a one-time analysis; document data sources, assumptions, model versions, and evaluation results.
 * Follow applicable institutional requirements for data access, privacy, security, IRB approvals, and data use agreements; coordinate with the appropriate governance and IT partners when projects involve sensitive data or system integration.
 * Contribute analytic methods and results to abstracts, manuscripts, grant proposals, and presentations; mentor students, residents, and collaborators as appropriate.
 * Communicate priorities, progress, risks, and dependencies to project teams and department leadership, including the Director of Data Science.