Biomedical Data Scientist - Single-Cell & AI Analytics

Harvard Medical School; Boston (MA); Hybrid; USD 120,000 - 160,000; Full time

Benefits offered by this job

Medical, dental, and vision

Tuition assistance

Wellbeing resources

Commuter benefits

Job summary

Harvard Medical School seeks an experienced Data Scientist/Computational Biologist to advance the Core for Computational Biomedicine (CCB) with scalable, reproducible analysis workflows. You will process and integrate single-cell and spatial multi-omics data, develop tools, and deliver data science products for research labs.

The role emphasizes proficiency in R or Python, strong quantitative skills, collaboration across teams, and opportunities for methodological creativity in data integration

Qualifications

• PhD or higher in bioinformatics, biostatistics, CS, or related field.
• Strong experience with genetic, genomic, or image data analysis.

Responsibilities

• Collaborate on design, implementation, documentation, and maintenance of scalable, reproducible analysis pipelines.
• Apply AI-enabled methods to accelerate research workflows with rigorous validation.
• Provide hands-on training, workshops, and materials to HMS researchers.
• Develop and maintain open-source software and data resources (Bioconductor, PyPI) and engage with external communities.

Skills

R

Python

Git

Machine learning

Statistics

Communication skills

Independent work

Single-cell omics

Education

PhD in Bioinformatics or related field

Tools

Bioconductor

PyPI

TensorFlow

PyTorch

RStudio Shiny

Git

Job description

Harvard Medical School seeks an experienced Data Scientist/Computational Biologist to advance the Core for Computational Biomedicine (CCB) with scalable, reproducible analysis workflows. You will process and integrate single-cell and spatial multi-omics data, develop tools, and deliver data science products for research labs.

The role emphasizes proficiency in R or Python, strong quantitative skills, collaboration across teams, and opportunities for methodological creativity in data integration