Research Assistant | Computational Biology

Mass General Brigham • Boston, MA

2d • $20 - $29 • Hybrid

About The Position

We are seeking a motivated and detail-oriented Research Assistant to join our interdisciplinary team. This role is ideal for a recent graduate with a background in Bioinformatics, Computer Science, or the Life Sciences who is eager to apply their computational skills to cutting-edge biological research. As a key member of our "dry lab," you will support the analysis of complex datasets—including single-cell RNA-seq and spatial transcriptomics—to help uncover new insights into disease biology, with a particular focus on oncology. You will work closely with both experimentalists and senior computational biologists, gaining hands-on experience in R, Python, and High-Performance Computing (HPC) environments. The successful candidate will be a proactive problem-solver who enjoys translating raw data into biological meaning and thrives in a collaborative, fast-paced research setting. This is an excellent opportunity to build a career at the intersection of data science and modern medicine.

Requirements

• Bachelor’s degree in Bioinformatics, Computational Biology, Computer Science, Molecular Biology, Statistics, or a related quantitative field.
• Solid understanding of bioinformatics workflows and a keen interest in applying them to real-world biological questions.
• Working knowledge of Python and R for data manipulation; familiarity with the Linux/Unix command line (Bash) is a plus.
• Ability to apply basic statistical methods to analyze and interpret biological data accurately.
• Basic exposure to—or a strong desire to learn—High-Performance Computing (HPC) or cloud-based analysis environments.
• Strong interpersonal skills with the ability to communicate technical concepts to colleagues with diverse backgrounds (e.g., bench scientists and lab technicians).
• A proactive learner who is comfortable working in a small, fast-paced team and eager to take on new computational challenges.

Nice To Haves

• Familiarity with single-cell RNA-seq or spatial transcriptomics through coursework, internships, or senior thesis projects is highly desirable.
• A passion for cancer research; any previous exposure to oncology datasets is a bonus but not a requirement.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level