Computational Genomics Research Scientist

Vertex Pharmaceuticals•Boston, MA

1d•Hybrid

About The Position

We are seeking a Computational Genomics Research Scientist to support the development of transformative cell and gene therapies across Vertex's research portfolio. This Boston-based position is part of the global Data and Computational Sciences organization and will collaborate closely with research teams spanning stem cell biology, immunology, gene editing, and pre-clinical development to design studies, execute and analyze complex genomic analyses, and ultimately generate biological insights that drive early research across multiple disease programs. As an embedded computational scientist, you will be responsible for performing bulk and single-cell sequencing analyses, spatial transcriptomics, and multiomic profiling to help uncover the molecular programs that govern therapeutic cell identity, differentiation, function, and safety to accelerate the development of next-generation cell and gene therapies.

Requirements

A proven track record in the analysis, visualization, and interpretation of genomic and next-generation sequencing (NGS) data.
Solid scientific understanding of gene editing, the role of genetic variation in human disease, molecular biology, and cellular biology.
Expertise with applying computational methods and bioinformatics tools to large-scale data.
Solid understanding of statistics.
Ph.D. in computational biology, bioinformatics, genomics, systems biology, biomedical engineering, statistics, computer science, or a related quantitative discipline, with 0-3 years of relevant postdoctoral or industry experience; or a Master's degree in a related field with 3+ years of relevant industry experience.
Demonstrated expertise in the design, analysis, visualization, and interpretation of single-cell genomics and spatial transcriptomics experiments.
Proficiency in Python and/or R for scientific computing and data analysis.
Experience working in Linux-based high-performance computing (HPC) and/or cloud computing environments.
Strong foundation in statistics, experimental design, and reproducible computational research.
Proven ability to derive biological insight from complex datasets and translate findings into actionable recommendations for research teams.
Excellent communication and presentation skills, with the ability to effectively convey complex scientific findings to multidisciplinary audiences.
Demonstrated ability to work collaboratively within cross-functional teams and contribute to a positive, team-oriented environment.

Responsibilities

Collaborate closely with cross-functional research teams to identify key scientific questions that can be addressed through genomics, sequencing, and bioinformatics approaches.
Lead the application and advancement of spatial transcriptomics methods, collaborating across teams and sites to develop, implement, and share analytical best practices.
Analyze and integrate diverse genomic datasets, including bulk RNA-seq, single-cell profiling, spatial transcriptomics, and other multiomic datasets to generate actionable insights.
Perform sophisticated computational analyses of sequencing and other biological data through deep critical thinking, incorporation of published findings and best practices from the field, strong statistical rigor, and data science visualizations.
Distill complex analyses and scientific results into key take-home messages and recommendations for next steps to a broad audience.
Partner with computational and software teams to improve analytical workflows, reproducibility, usability, and robustness.
Practice reproducible research and maintain well-documented, version-controlled analytical workflows.
Author study reports and technical documentation to support research and regulatory activities.
Evaluate emerging genomics technologies and computational methods and incorporate new approaches where appropriate.