Research Data Analyst 2: Rare-Disease EHR Data Scientist

Stanford University; Palo Alto (CA); Hybrid; USD 90,000 - 130,000; Full time

Job summary

Stanford University’s Division of Computational Medicine seeks a Research Data Analyst 2 to help reveal how rare diseases emerge in patients’ medical histories. You will work with electronic health records from Stanford patients with confirmed diagnoses to identify cohorts and reconstruct diagnostic journeys across ten rare diseases.

You will write Python and SQL, build tools, and collaborate with faculty, clinicians, and researchers.

Qualifications

• Bachelor's degree and three years of relevant experience.
• Experience in a quantitative discipline such as economics, finance, statistics or engineering.
• Strong MS Office and analytical software proficiency.

Responsibilities

• Develop and evaluate methods to identify rare-disease cohorts using structured EHR data and clinical text.
• Build reproducible pipelines that organize clinical events into patient diagnostic timelines.
• Create annotation and review tools that help clinicians validate extracted information and guide improvements.
• Investigate data-quality issues and extraction errors, using clinical feedback to refine computational methods.
• Develop performance metrics and automated tests for timeline extraction and downstream disease-risk models.
• Translate analyses into clear datasets, figures, reports, presentations, and manuscript contributions.
• Document methods, validation results, and limitations so research findings can be understood and reproduced.
• Collaborate with clinical and technical colleagues to shape methods, interpret results, and advance the research.

Skills

MS Office

Analytical skills

Writing skills

Prioritization

Education

Bachelor's degree

Job description

Stanford University’s Division of Computational Medicine seeks a Research Data Analyst 2 to help reveal how rare diseases emerge in patients’ medical histories. You will work with electronic health records from Stanford patients with confirmed diagnoses to identify cohorts and reconstruct diagnostic journeys across ten rare diseases.

You will write Python and SQL, build tools, and collaborate with faculty, clinicians, and researchers.