Research Data Analyst

About the role

The Research Data Analyst will work under the direction of the Principal Investigator to support collaborative research integrating genomic and multi-omic data with clinical and electronic health record (EHR) data. The analyst will contribute to projects investigating the genetic, epigenetic, and downstream molecular basis of psychiatric and related complex traits.

Responsibilities

• Perform statistical analyses of genomic and molecular data, including polygenic risk score (PRS) analyses and association testing.
• Integrate genetic, transcriptomic, methylomic, clinical, and EHR phenotype data to investigate relationships between molecular variation and clinical outcomes.
• Analyze high-dimensional transcriptomic and DNA methylation datasets.
• Train genetically regulated gene expression (GReX) models and conduct transcriptome-wide association studies (TWAS).
• Perform differential expression and association analyses using statistical frameworks such as limma and related R/Bioconductor tools.
• Organize, curate, and analyze clinical and EHR-derived phenotype data, including data collection, harmonization, quality control, and phenotype definition.
• Develop and maintain reproducible analysis pipelines in R and/or Python.
• Conduct data quality control, visualization, statistical modeling, and interpretation of results.
• Organize, document, and maintain research datasets and analysis outputs to facilitate reproducibility and collaboration.
• Work closely with investigators on study design, analysis, interpretation, and preparation of results for presentations and manuscripts.

Qualifications

• Bachelor’s or Master’s degree in bioinformatics, biostatistics, computational biology, data science, statistics, genetics/genomics, or a related quantitative field required.
• Experience attained through education or 0–1 year of relevant research or data-analysis experience required. Academic research, internships, or coursework involving statistical analysis or biomedical data may satisfy this requirement.

Knowledge, Skills and Abilities

• Proficiency in R and statistical data analysis; experience with Python is desirable.
• Experience working with large, complex, or high-dimensional biomedical datasets.
• Familiarity with genomic, transcriptomic, DNA methylation, or other molecular data.
• Familiarity with PRS, GWAS, TWAS/GReX, limma, or related genomic analysis methods is desirable.
• Familiarity with clinical or EHR data, including phenotype curation and harmonization, is desirable.
• Understanding of statistical modeling, data visualization, and data quality-control methods.
• Strong organizational skills and the ability to maintain well-documented and reproducible analyses.
• Ability to work collaboratively with investigators, scientists, clinicians, and interdisciplinary research teams.
• Ability to identify analytical or data-related roadblocks and seek appropriate guidance.
• Strong computer skills and careful attention to data accuracy and organization.