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Lead Computational Genetics Analyst

2 months ago

Slough, Slough, United Kingdom Lifelancer Full time
Job Summary

We are seeking a highly skilled Principal Computational Genetics Scientist to join our global biopharma client based in Slough on a contract basis initially for 12 months. The role will operate on a hybrid basis, working 2 days in the office and 3 days from home. There is a high likelihood of extension for the right person depending on performance and impact.

About the Role

The successful candidate will support the analytical workload within the genetics group, which supports the Research and Development (R&D) pipeline ranging from target identification to clinical development and post-marketing. The group is small but dynamic, developing and applying leading-edge analytics and approaches to answer a diverse set of questions.

Key Responsibilities
  • Support the analytical workload within the genetics group, including data analysis and interpretation.
  • Develop and apply advanced computational skills, including proficiency in R, Python, SQL, and bash, and experience working in a cloud environment.
  • Collaborate with the team to design and implement statistical genetics approaches.
  • Present data and results to stakeholders, including senior management and external partners.
  • Stay up-to-date with the latest developments in computational genetics and apply this knowledge to improve processes and outcomes.
Requirements
  • Direct experience of analyzing and using genetic data generated through multiple platforms and associated analytical approaches.
  • Expert knowledge of statistics, with specific experience in and application of statistical genetics.
  • Advanced computational skills, including proficiency in R, Python, SQL, and bash, and experience working in a cloud environment.
  • Ability to interpret and present data in the context of the question under study.
  • Good knowledge of the drug discovery process.
  • Problem-solving skills.
Preferred Qualifications
  • Experience of the application of genetics and genetic approaches to drug discovery and development.
  • Experience of using and querying population databases.
  • Expertise in methods for functional interpretation of genomics data, such as variant and gene pathway mapping and enrichment.
  • Ability to understand and integrate data from other sources, such as transcriptomics and proteomics.
  • Expertise in the application of machine learning or artificial intelligence approaches.
  • Relevant biology expertise.
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