Bioinformatics Analyst

Position: Bioinformatics Analyst – Proteomics & Multi-Omics

Albert Einstein College of Medicine, Department of Biochemistry & Proteomics Core

We are seeking a motivated and detail-oriented Bioinformatics Analyst to join the Sidoli Laboratory and Proteomics Core at the Albert Einstein College of Medicine. This entry-level position offers the opportunity to work at the interface of experimental and computational biology, contributing to cutting-edge biomedical research projects in aging, chromatin biology, cancer, metabolism, and single-cell proteomics. The analyst will support data processing, analysis, and integration across proteomics, transcriptomics, and other high-throughput biological datasets generated in close collaboration with research groups across the institution.

Key Responsibilities

• Process, analyze, and visualize mass spectrometry-based proteomics data (DDA, DIA, SILAC, single-cell proteomics).

• Develop and maintain reproducible computational pipelines using Python, R, and shell scripting.

• Implement statistical and machine-learning approaches for high-dimensional data integration.

• Collaborate with faculty, postdocs, and students to interpret results and generate mechanistic insights.

• Maintain accurate documentation of analyses, workflows, and results for publication and reproducibility.

• Support data management, including version control, large-scale storage, and compliance with institutional data security policies.

• Contribute to lab meetings, grant proposals, and manuscript preparation.

Qualifications

Required

• Bachelor’s degree in Bioinformatics, Computational Biology, Computer Science, or related field.

• Familiarity with proteomics or high-throughput sequencing data analysis.

• Basic programming skills in Python, R, or comparable languages.

• Experience working in Unix/Linux environments and using version control (e.g., Git).

• Analytical and problem-solving skills.

• Excellent communication and organizational abilities.

Preferred

• Experience with proteomics data analysis tools (e.g., MaxQuant, DIA-NN, Spectronaut, Skyline) and/or RNA-seq analysis frameworks.

• Knowledge of statistical modeling and data visualization for biological applications.

• Exposure to HPC clusters or cloud computing environments.

• Familiarity with reproducible research practices (e.g., R Markdown, Jupyter, Docker/Singularity).