Principal Research Scientist, Computational Genomics Lead for Oxford Research Site

To advance the development of transformative therapies for people with serious and life-threatening diseases, we are seeking a computational biology leader with expertise in making biological discoveries by leveraging large-scale genomic and genetic data.  This position is part of a global Computational Genomics team that designs and analyzes genomic studies to advanceVertex’s drug discovery programs.  As the Computational Genomics lead(individual contributor) at Vertex’s Oxford UK Research site, you will collaborate closely with the Oxford Research groupin designing and executing genomic studies,toimpactmultiple disease projects across the drug discovery pipeline using genomics. Example areas of impactinclude targetvalidation, pre-clinical model characterization, andunderstanding mechanisms of actions of novel therapeutics.  Your key role will be to interface with leadership and disease projectteams to align on the priority and design of genomic studies, with the aim ofadvancingthe diseaseresearch programs at the Oxford Research site. You will also collaborate closely with the broader Computational Genomics team across Global Research todrive the execution of genomic studies through best-practice computational analyses.  This is an on-site or hybrid eligible position based out of our site in Oxford, UK, requiring at least 3 days/week onsite. 

Key responsibilities: 

• As the Computational Genomics lead for the Oxford researchsite, proactively collaborate with Oxford disease project teamsto identify key project questions or challenges that can be answered using genomics, sequencing, or bioinformatics contributions.  This is an individual contributor role with no direct reports at this time.   

• Partner with Computational Genomics leadsat our Boston and San Diego toprioritize global computational genomic needs to maximize impact across the global research portfolio. 

• Serve as the accountable lead for the execution of prioritized computational genomic activities in the Oxford UK research site.  Interface with Oxford leadership to provide regular progress updates. 

• Perform advanced computational analyses of large-scale sequencing data and other biological data, through deep critical thinking, published findings and best practices from the field, strong statistics, and data science visualizations.  

• Distil complex analyses and scientific results into key take-home messages and recommendations for next steps to a broad audience. 

• Partner across global computational genomics team to develop guidelines for best practices for key genomic analyses. 

• Partner with genomics method development team and global computational genomics leadership to prioritize, develop, and evolve computational methods. 

Minimum Qualifications: 

• Relevant education in computational biology, bioinformatics, human genetics, genomics, systems biology, bioengineering, or a related field, and relevant industry or post-doctoral experience.   

• A proven track record in the analysis, visualization, and interpretation of large-scale human genetic data in a drug development context. 

• Demonstrated ability to work across cross-functional teams to design and implement experiments, andexecute computational analysis. 

• Expertise in applying computational methods and bioinformatics tools to large-scale data; proficiency with R or Python, Linux/Unix systems, and high-performance computing systems.  Experience with workflow language and cloud computing is a plus.

• Solid understanding of statistics and genetics, including the role of genetic variation in human disease.

• Demonstrated ability to work across cross-functional teams, ensuring timely progress.

• Excellent presentation and communication skills, including the ability to tailor scientific content to audiences with different backgrounds.

• A team-oriented growth mindset that welcomes feedback from others and supports other team members; a positive attitude that enthusiastically tackles and overcomes challenges.