Sr. Bioinformatics Analyst

The Nima Sharifi Laboratory is seeking a highly motivated Sr. Bioinformatics Analyst to support translational cancer research focused on steroid metabolism, androgen signaling, therapeutic resistance, and biomarker discovery in prostate cancer. The successful candidate will work closely with analytical chemists, mass spectrometrists, biologists, and clinicians to process, analyze, and interpret LC-MS, imaging mass spectrometry, and multi-omic datasets.

• Develop and maintain reproducible computational pipelines for metabolomics and imaging mass spectrometry data analysis.
• Process and analyze untargeted and targeted LC-MS metabolomics datasets.
• Analyze spatial metabolomics data generated by MALDI imaging mass spectrometry (MALDI-MSI) and related imaging platforms.
• Integrate molecular and clinical datasets to identify biomarkers associated with treatment response, disease progression, and patient outcomes.
• Perform statistical analyses and develop reproducible pipelines using modern bioinformatics tools and programming languages.
• Contribute to studies investigating androgen biosynthesis, steroid metabolism, androgen receptor signaling, and mechanisms of therapeutic resistance in prostate cancer.
• Support analysis of patient‑derived datasets, clinical cohorts, and public cancer genomics resources including TCGA, cBioPortal, GEO, dbGaP, and other consortium datasets.
• Generate publication‑quality visualizations, heatmaps, spatial ion images, pathway analyses, and statistical reports.
• Collaborate with laboratory investigators on experimental design, data interpretation, and manuscript preparation, and grant applications.

• Bachelor’s degree in relevant field required, M.S. or Ph.D. preferred

• Demonstrated experience analyzing metabolomics and next-generation sequencing data.
• Proficiency in R and/or Python for statistical analysis and data visualization.
• Experience with metabolomics software such as MS-DIAL, XCMS, MZmine, Skyline, Compound Discoverer, Metabo Analyst, or similar platforms.
• Familiarity with metabolite identification, pathway analysis, and isotope tracing workflows is preferred.
• Experience with MALDI imaging mass spectrometry (MALDI-MSI), DESI-MSI, or other spatial metabolomics platforms is preferred.
• Experience integrating metabolomics with transcriptomic, proteomic, or clinical datasets is desirable.
• Strong understanding of statistics, experimental design, data quality assessment, and reproducible computational research.
• Excellent written and verbal communication skills and the ability to work effectively in a multidisciplinary research environment.