Research Data Analyst - Limited FTE

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Onsite or Remote

Fully On-Site

Work Schedule

Variable hours between Monday through Friday, 8:00am - 5:00pm

Posted Date

09/17/2026

Salary Range
: $35.66 - 73.02 Hourly

Employment Type

4 - Staff:
Limited

Duration

08/26/2027

Job #

32857

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We are seeking a detail oriented Data Analyst to support high impact research using large healthcare datasets, including UCLA and VA electronic health records (EHR), Medicare data, and other complex clinical databases. The analyst will play a key role in building and managing research datasets, performing data cleaning, and conducting advanced statistical analyses.

Key Responsibilities:

• Construct, manage, and clean large, complex datasets (UCLA/VA EHR, Medicare, and others).
• Collaborate with faculty and research staff on data quality control and database design.
• Develop and apply statistical models to support clinical and health services research.
• Summarize results in clear graphical and tabular formats for reports, publications, and presentations.
• Partner with research teams to ensure accurate, reproducible, and well documented analyses.

This role is ideal for candidates who enjoy transforming raw data into meaningful insights that drive impactful healthcare research.

This is a 5% FTE limited position, which may convert to career.

Salary: $35.06 - $73.02hourly

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Required:

• Bachelor's Degree in Statistics / Computer Science / Bioinformatics / Data Specification / Data Science / Computational and/or equivalent combination of education and experience.
• Knowledge of basic and multivariable statistics.
• Experience with a range of statistical modeling (e.g., hierarchical/multilevel modeling, GLM and GEE modeling, two-stage models, survival models).
• Knowledge of research design.
• Competency in statistical software applications (e.g., STATA, R, SAS).
• Experience with database management via SQL to run queries and extract data.
• Ability to conceptualize and construct tables and graphs.
• Working knowledge of statistical terminology.
• Ability to summarize statistical analyses for collaborators.
• Ability to manage and merge large dataset and data tables from different sources to develop analytic datasets as a single file.
• Ability to produce visual tools that highlight the essential information from a project or study.
• Ability to learn new statistical techniques and to adapt existing code to customize statistical analysis strategies.

Required:

• Bachelor's Degree in Statistics / Computer Science / Bioinformatics / Data Specification / Data Science / Computational and/or equivalent combination of education and experience.
• Knowledge of basic and multivariable statistics.
• Experience with a range of statistical modeling (e.g., hierarchical/multilevel modeling, GLM and GEE modeling, two-stage models, survival models).
• Knowledge of research design.
• Competency in statistical software applications (e.g., STATA, R, SAS).
• Experience with database management via SQL to run queries and extract data.
• Ability to conceptualize and construct tables and graphs.
• Working knowledge of statistical terminology.
• Ability to summarize statistical analyses for collaborators.
• Ability to manage and merge large dataset and data tables from different sources to develop analytic datasets as a single file.
• Ability to produce visual tools that highlight the essential information from a project or study.
• Ability to learn new statistical techniques and to adapt existing code to customize statistical analysis strategies.

• Master’s degree or higher in Biostatistics/Statistics/Computer Science or related field
• 2 years experience in Statistics/Computer Science/Bioinformatics/Data Specification/ Data Science.
• Competence with Python for database management and statistical analyses.
• Experience with using Jupyter notebook
• Experience with regular expression natural language processing via Python
• Experience with machine learning models (e.g. regularization regression, random forests, ensemble methods)
• Experience with causal inference approaches such as instrumental variable models, propensity-score methods, difference-in-difference designs, regression discontinuity, G-estimation.
• Experience with economic models.