We are seeking a motivated Computational Genomics Scientist with expertise in NGS and SNP array data analytics to join Vertex’s research site in Oxford, UK. At Vertex, we put much emphasis on human genetic and genomic data for the discovery and validation of therapeutic targets. We are therefore looking for an individual to join our global Computational Genomics team who will be enthusiastic about identifying and evaluating new targets in existing or new therapeutic areas. Prior experience analyzing genetic data to evaluate genotype-phenotype relationships is critical. Hands-on experience analyzing additional omics data, such as RNA-Seq or proteomic datasets, and/or experience in functional genomics (including wet lab experience) is also a plus.
This position is part of a rapidly growing global Computational Genomics function focused on delivering on the power of genomics to accelerate all areas of the drug discovery lifecycle. Your key tasks will be to collaborate with cross-functional project teams and/or external (academic) collaborators to design studies, analyze and interpret large-scale genomics data, and communicate results that will have critical impact on the development of cutting-edge, transformative therapies. You will play a key role in bringing together the needs of project teams with state-of-the-art computational biology tools. The role will require a proactive communication style and seamless collaboration with both computational and experimental colleagues within and external to the organization.
• Plan studies leveraging genetic and genomic data to answer critical biological questions and accelerate the development of transformational therapies in existing Vertex programs or new disease areas
• Execute and iterate on analysis of large-scale sequencing or genotyping data, in partnership with computational and experimental team members as well as (external) collaborators
• Collaborate with project teams, acting as the computational biology expert, to identify key needs and questions that can be addressed using genomics, sequencing, or bioinformatics in general
• Visualize, summarize, and communicate findings to project teams and other key stakeholders
• Follow relevant cutting-edge advancements in the fast-paced field of genomics, bringing developments in-house or building upon them as appropriate
• Partner with team members to improve or expand computational methods and develop best-practice, easy-to-use analytical workflows to answer common genomics questions
• PhD or exceptional MS with experience in computational biology, statistical genetics, population genetics, bioinformatics, bioengineering, machine learning or a related field
• A proven track record in the analysis, visualization, and interpretation of genomic and next-generation sequencing (NGS) data
• Demonstrated ability to work closely with project teams and/or experimental collaborators to design studies and develop analyses to answer scientific questions
• Familiarity with applying computational methods and bioinformatics tools to large- scale data including proficiency with Linux/Unix systems and high-performance computing environments
• Familiarity with relevant analytical approaches and underlying assumptions
• Detail-oriented and self-motivated approach to problem solving with excellent reasoning skills
• A team-oriented growth mindset that welcomes feedback from others and supports other team members; strong collaboration skills to work across teams and functions
• A positive attitude that enthusiastically tackles and overcomes challenges
• Strong organizational and time-management skills to prioritize needs and get things done
• Excellent presentation and communication skills, including the ability to tailor scientific content to audiences with different backgrounds
Preferred Additional Qualifications:
• Solid scientific understanding of the role of genetic variation in human disease, molecular biology, and cellular biology including specialized knowledge in one or more disease areas
• Expertise with developing analysis pipelines including coding proficiency with R, Python, or a related high-performance language.
• Strong foundation in relevant statistical principles underlying analytical approaches including some experience in methods development