Bioinformatics Analyst

Albert Einstein College of Medicine • New York, NY

12d • Onsite

About The Position

The Chang Lab at Albert Einstein College of Medicine is seeking a highly motivated Bioinformatics Analyst to join a collaborative computational biology research program focused on developing standardized analytical pipelines that transform multi-omics datasets into mechanistic insight for human disease. The successful candidate will contribute to funded projects at Albert Einstein College of Medicine aimed at integrating transcriptomic, proteomic, metabolomic, and lipidomic data through genome-scale metabolic network modeling and machine learning to identify disease mechanisms, biomarkers, and therapeutic targets.
The position centers on software development, computational modeling, and collaborative analysis of large-scale biological datasets. The successful candidate will work closely with experimental investigators across multiple disease-focused collaborations and will play a central role in developing computational infrastructure that supports hypothesis generation from multi-omics data.

Requirements

• Bachelor's or Master's degree in Bioinformatics, Computational Biology, Biomedical Engineering, Computer Science, Applied Mathematics, or a related quantitative discipline, and at least 2 years of computational research experience after graduation.
• Strong programming experience in Python, MATLAB, and R.
• Experience developing reproducible computational analysis pipelines.
• Experience working in Linux/Unix environments and with version control systems.
• Familiarity with high-performance computing environments, workflow automation, and parallel computing.
• Excellent written and verbal scientific communication skills.
• Ability to work largely independently but with periodic guidance from PI and more senior lab members.

Nice To Haves

• Experience analyzing next-generation sequencing data, particularly RNA-seq.
• Familiarity with metabolomics, proteomics, lipidomics, or other multi-omics datasets.
• Experience applying machine learning methods to biological data.
• Experience with genome-scale metabolic modeling, constraint-based modeling, COBRA Toolbox/COBRApy, or related systems biology methods.
• Prior experience contributing to collaborative biomedical research and scientific publications.

Responsibilities

• Implement, document, and maintain standardized computational pipelines for analysis of multi-omics datasets, with particular emphasis on bulk RNA-seq and integration with proteomic, metabolomic, and lipidomic data.
• Build computational workflows based on genome-scale metabolic network models for identifying metabolic biomarkers, context-specific metabolic model reconstruction, and performing constraint-based flux analyses to generate mechanistic hypotheses.
• Apply established machine learning workflows for multi-omics integration, including feature selection, phenotype classification, latent network inference, and survival prediction using methods such as random forests, mixed graphical models, and related statistical learning techniques.
• Adapt computational pipelines for efficient execution on a high-performance computing (HPC) environment, including workflow automation and parallel computing.
• Apply these computational methods to collaborative studies investigating metabolic adaptations across multiple disease models, including diabetes, cancer dormancy, immune cell development, and neurodegenerative disease.
• Interpret computational results through collaboration with experimental scientists to generate biologically meaningful and experimentally testable hypotheses.
• Contribute to dissemination of methods and results through manuscripts and presentations.

Benefits

• Albert Einstein College of Medicine considers factors such as (but not limited to) scope and responsibilities of the position, candidate's work experience, education/training, key skills, internal peer equity, as well as, market and organizational considerations when extending an offer.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: Associate degree