Senior Metabolomics Bioinformatics Scientist

Georgetown University; Washington (District of Columbia); On-site; USD 48,000 - 88,000; Full time

Job summary

Georgetown University’s Metabolomics Shared Resource (MSR) seeks a Bioinformatics Scientist to design experiments and perform data analysis for LC-MS/GC-MS based metabolomics and lipidomics studies. The role involves developing pipelines to process raw data, performing QC, normalization, and complex statistical analyses in collaboration with wet-lab scientists.

You will integrate multi-omics data including transcriptomics and proteomics and support experimental design.

Qualifications

• MS in bioinformatics/biostatistics/computational biology (PhD preferred).
• Experience analyzing multi-dimensional omics data.
• Experience with R/Bioconductor.
• Strong written and oral communication skills; ability to prioritize and multi-task.
• Ability to work independently in a team-based environment.

Responsibilities

• Process raw LC-MS and GC-MS data using a well-defined analytical platform for downstream statistical analysis.
• Develop and maintain integrated bioinformatics pipelines for data pre- and post-processing.
• Perform advanced statistical and bioinformatics analyses of high-dimensional omics datasets (metabolomics, lipidomics).
• Communicate results to clients and participate in study design discussions.
• Keep up-to-date with developments and implement relevant tools and workflows.

Skills

Multi-dimensional omics data analysis

R/Bioconductor

Data science research

Strong communication skills

Independent and team work

Wet-lab experience

Education

MS in bioinformatics/biostatistics/computational biology

PhD preferred

Tools

R/Bioconductor

Job description

Georgetown University’s Metabolomics Shared Resource (MSR) seeks a Bioinformatics Scientist to design experiments and perform data analysis for LC-MS/GC-MS based metabolomics and lipidomics studies. The role involves developing pipelines to process raw data, performing QC, normalization, and complex statistical analyses in collaboration with wet-lab scientists.

You will integrate multi-omics data including transcriptomics and proteomics and support experimental design.