Sr. Bioinformatics Analyst

University of Miami Miller School of Medicine; Miami (FL); On-site; USD 70,000 - 100,000; Full time

Job summary

The University of Miami Miller School of Medicine is looking for a highly motivated Sr. Bioinformatics Analyst dedicated to supporting translational cancer research focused on steroid metabolism and therapeutic resistance.

You will collaborate with biologists and clinicians, analyze LC-MS datasets, and develop computational pipelines for data analysis. A Bachelor’s degree is required, with a preference for higher degrees. Strong skills in programming and statistics are essential for this role.

Qualifications

• Bachelor’s degree in a relevant field required, M.S. or Ph.D. preferred.
• Demonstrated experience analyzing metabolomics and next-generation sequencing data.
• Experience with metabolomics software such as MS-DIAL, XCMS, or similar.

Responsibilities

• Develop and maintain computational pipelines for metabolomics data analysis.
• Process and analyze LC-MS metabolomics datasets.
• Generate publication-quality visualizations and statistical reports.

Skills

Proficiency in R and/or Python for statistical analysis

Experience analyzing metabolomics data

Strong understanding of statistics

Excellent written and verbal communication skills

Education

Bachelor’s degree in relevant field

M.S. or Ph.D.

Tools

MS-DIAL

XCMS

MALDI imaging mass spectrometry

Job description

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.

Key Responsibilities

• 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.

Education

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

Experience

• 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, MetaboAnalyst, 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.