Research Data Analyst ; Hybrid

Position: Research Data Analyst 2 (Hybrid Opportunity)
Job-:
Information Analytics

Location:

Stanford, CA, United States Workplace:
Hybrid

Contract:

Regular Posted:
Pay Range: $91,852-$171,457

Pay Frequency:
Annually Fixed Term: NStanford Schools and Units:
School of Medicine Full time or Part time:
Full Time Stanford’s Division of Computational Medicine brings together expertise in biomedical informatics, data science, and medicine to tackle complex questions about human health. We develop computational and statistical methods that help researchers make sense of health data and generate evidence for clinical and translational research.

We are seeking a Research Data Analyst 2 to help reveal how rare diseases emerge in patients’ medical histories. Working with electronic health records from Stanford patients with confirmed diagnoses, you will develop methods to identify patient cohorts and reconstruct diagnostic journeys across 10 rare diseases. Your work will connect information from clinical notes, diagnoses, procedures, medications, and laboratory results to support research on disease recognition and risk  this role, you will contribute throughout the research process, from translating clinical questions into computational methods to evaluating results and sharing findings in publications and presentations.

You will write Python and SQL code, build tools that bring clinical expertise into method development, and work closely with faculty, clinicians, and technical researchers. This is an opportunity to apply your programming and analytical skills to clinically meaningful questions while deepening your experience in biomedical data science.

Primary 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.

This position offers a hybrid schedule, with onsite work three days per week, subject to operational needs. Please submit a resume and cover letter describing your relevant experience and interest in computational approaches to rare-disease research.

Responsibilities Core Duties:

Prioritize and extract data from a variety of sources such as
notes, survey results, medical reports, and laboratory data, and
maintain its accuracy and completeness.

Determine additional data collection and reporting
requirements.

Design and customize reports based upon data in the database.
Oversee and monitor regulatory compliance for utilization of the
data.

Use system reports and analyses to identify potentially
problematic data, make corrections, and eliminate root cause for data
problems or justify solutions to be implemented by others.

Create complex charts and databases, perform statistical
analyses, and develop graphs and tables for publication and
presentation.

Serve as a resource for non-routine inquiries such as requests
for statistics or surveys.

Test prototype software and participate in approval and release
process for new software.

Provide documentation based on audit and reporting criteria to
investigators and research staff.

Minimum Education:

Bachelor's degree and three years of relevant experience or
combination of education and relevant experience.

Minimum Experience:

Experience in a quantitative discipline such as economics, finance,
statistics or engineering.

Knowledge, Skills and Abilities:

Substantial experience with MS Office and analytical
programs.

Excellent writing and analytical skills.

Ability to prioritize workload.