Computational Associate II Multi-Omics & Single-Cell Insight

Broad Institute of MIT and Harvard; Cambridge (MA); On-site; USD 58,000 - 81,000; Full time

Benefits offered by this job

Medical, dental, vision insurance

401(k) retirement plan

Paid time off

Life & disability insurance

13 paid holidays

Job summary

The Broad Institute of MIT and Harvard is seeking a Computational Associate II to provide computational support for multi-omic research in the Xavier Lab and Klarman Cell Observatory. You will analyze single-cell and spatial multi-omics datasets, develop new methods, and collaborate with computational and experimental biologists across affiliated institutions.

Responsibilities include building pipelines, planning project requirements, writing scalable code, and presenting progress.

Qualifications

• BS+2+ years with publication record or MS in relevant field
• Python or R and ML knowledge
• Experience analyzing biological data (single-cell/spatial)
• Independence, creativity, attention to detail, strong analytical skills
• Excellent communication and cross-disciplinary collaboration
• Ability to manage multiple tasks and priorities

Responsibilities

• Analyze data across collaborations (scRNA-seq, snATAC-seq, spatial transcriptomics, proteomics, metabolomics, microbiome)
• Develop, enhance, and maintain bioinformatics pipelines
• Plan project requirements and timelines with the team
• Write maintainable, scalable code
• Learn new technical skills and biological knowledge
• Present project progress in group meetings
• Lead projects leading to first-author publications

Skills

Python

R

ML

Deep learning

Education

BS or MS in CS/Engineering/Biology/Bioinformatics

Tools

Bioinformatics pipelines

Single-cell data analysis

Spatial transcriptomics

Job description

The Broad Institute of MIT and Harvard is seeking a Computational Associate II to provide computational support for multi-omic research in the Xavier Lab and Klarman Cell Observatory. You will analyze single-cell and spatial multi-omics datasets, develop new methods, and collaborate with computational and experimental biologists across affiliated institutions.

Responsibilities include building pipelines, planning project requirements, writing scalable code, and presenting progress.