Data Scientist/Computational Biologist

The Core for Computational Biomedicine (CCB) in the Department of Biomedical Informatics (DBMI) at Harvard Medical School (HMS) is looking for an experienced Data Scientist/Computational Biologist to advance in CCB’s mission to leverage data and computation to transform research and improve health. CCB provides computational and analytic resources to advance scientific discovery within HMS through its multi-disciplinary team of computational and quantitative scientists who work on collaborative projects both within the center and with members of the HMS community. The role will involve processing, analyzing, and integrating public and newly generated single-cell and spatial multi-omics datasets in collaboration with experimental labs at HMS. This will include developing sustainable tools, software packages, and integrated data science products that empower research labs to explore, analyze, and interpret their data. The data sources will often be at the leading edge of scientific discovery and will therefore require methodological work, algorithm development, and technical developments. The ideal candidate will be proficient in R and/or Python, have strong quantitative, analytical, and communication skills, and will be able to work independently and collaboratively on scientific problems and deliver solutions. There will be opportunities for working in teams and independent decision making at all levels of bioinformatic processing and statistical analysis of the data, as well as examining, evaluating, and recommending analytical approaches to collaborating labs. In addition, methodological developments for novel and challenging data analysis and integration tasks arise frequently requiring originality and creativity, including designing and analyzing follow-up experiments.

Responsibilities include: 

Collaboration on development and maintenance of scalable, reproducible pipelines

• Collaborate with the CCB team to design, implement, document, and maintain robust analysis workflows (e.g., QC, integration, statistical modeling, reporting) to support repeatable, high-quality computational research across multiple projects.

AI-enabled methods and applied research tooling 

• Apply knowledge of AI and computational research methods to evaluate and implement AI-assisted approaches that accelerate research workflows (e.g., knowledge extraction, annotation support, literature review and writing workflows) while ensuring scientific rigor, reproducibility, and responsible use.

Training, workshops, and internal enablement 

• Provide technical guidance and deliver hands-on workshops and learning materials for HMS researchers; provide office hours and consultation to promote best practices in computational biology, reproducible research, and AI-enabled workflows.

Open-source software and biomedical data asset development

• Building and maintaining open-source software and data resources (including distribution through established ecosystems such as Bioconductor and PyPI), support releases and user documentation, and engage with external developer communities to increase adoption and impact.