Salary/Position Classification

• 27.30 hourly, Non-Exempt (Eligible for overtime)
• Up to 19 hours per week hours per week
• 100% Remote Work Availability: No
• Hybrid Work Availability (requires some time on campus): No

Purpose of Position

We seek a part-time Data Analyst to join our collaborative, interdisciplinary team for the summer. Our lab investigates the epidemiology and underlying mechanisms of diabetic complications, with a particular focus on diabetic peripheral neuropathy. A current research direction examines the potential protective effect of vaccine-induced immunity against the development of peripheral neuropathy and seeks to elucidate the linkages and biological mechanisms involved.

Duties & Responsibilities

• Analyzing large-scale health datasets, including electronic health records (EHR), national survey data (e.g., BRFSS), and the NIH All of Us Research Program data (https://allofus.nih.gov)
• Applying statistical and machine learning approaches to identify factors contributing to diabetic complications (particularly peripheral neuropathy) and to evaluate the protective role of prior vaccinations, including longitudinal and survival analysis
• Supporting reproducible research workflows in R and/or Python (e.g., RMarkdown, Jupyter, version control)
• Helping to investigate potential mechanistic linkages between vaccination, immune-related factors, and protection against diabetic peripheral neuropathy. 

Required Competencies

• Programming skills are required: Python and/or R, with the ability to work with tabular health data. Statistical/analytical reasoning is required.
• Excellent interpersonal and presentation skills, with the ability to interface and communicate effectively with team members from diverse backgrounds.
• Excellent planning, time management, organizational, and work coordination skills are needed.

Minimum Requirements

• Demonstrated proficiency in Python and/or R programming (acquired through coursework, internships, or research projects).
• Statistical training, including coursework or hands-on experience in statistics, biostatistics, or quantitative methods (e.g., regression, hypothesis testing, study design).
• Hands-on experience with the NIH All of Us Research Program (e.g., Researcher Workbench, OMOP CDM), evidenced by completed research projects, conference posters or presentations, course/capstone projects, or other concrete deliverables that demonstrate working knowledge of the dataset.
• Ability to handle, clean, and analyze structured tabular data (CSV files, databases, or similar), with strong analytical reasoning and attention to data quality.
• A cover letter, a complete CV, and an appendix demonstrating prior hands-on work with the NIH All of Us Research Program (e.g., conference poster, GitHub link, code samples, or a research/class project report) are required. 
• Successful completion of a Criminal History Background Check

In compliance with federal law, all persons hired will be required to verify identity and eligibility to work in the US and to complete the required employment eligibility verification form upon hire. This position does not support visa sponsorship for continued employment.

Preferred Qualifications

• Experience with electronic health record (EHR) data, epidemiological datasets, or other large-scale public health data (e.g., BRFSS, NHANES, MIMIC, cohort studies) is highly preferred.
• Familiarity with longitudinal/survival analysis, causal inference methods, or machine learning applied to health data 
• Familiarity with reproducible research workflows (e.g., RMarkdown, Jupyter, Git/GitHub) 

To Apply

A cover letter, a complete CV, and an appendix demonstrating prior hands-on work with the NIH All of Us Research Program (e.g., conference poster, GitHub link, code samples, or a research/class project report) are required. Applications missing any of these materials will not be considered.