Director – Oncology Genomics, HGG
GSK
full-time
Posted on:
Location Type: Hybrid
Location: Stevenage • 🇬🇧 United Kingdom
Visit company websiteJob Level
Lead
Tech Stack
Python
About the role
- Lead and build a growing team of Oncology Computational Biologists and Genomicists to support and innovate the Oncology portfolio pipeline
- Contribute scientific expertise and design to multiple drug discovery or development efforts across oncology
- Act as a competent and recognized expert in developing and critiquing the scientific validity of research/development initiatives to drive the development and delivery of long-term scientific strategy in Oncology Genomics
- Individually contribute with analyses and integration of complex cancer datasets. This includes analysis of gene & protein expression (bulk + single cell + spatial), somatic mutations, copy number alterations, and structural variants
- Apply statistical methods and AI/machine learning algorithms to identify patterns in data, predict drug response, and discover potential biomarkers. Critically evaluate results.
- Conduct survival analysis to assess the impact of various factors on patient outcomes
- Lead the development and use of bioinformatics pipelines
- Serve as a recognized leader in driving technological foresight within specific scientific function or directing content of programs
- Lead and influence the outcome of multidisciplinary meetings including partners in therapeutic research units, translational and clinical development teams.
Requirements
- PhD with postdoc or industry experience OR MS with extensive years of related experience in Computational Biology, Bioinformatics, Cancer Biology, or a related field
- Proven track record of leadership or matrix leadership experience in Computational Biology, Cancer Biology or a related field
- Strong understanding of cancer biology and the drug discovery process.
- Proficiency in Python or R, with experience building bioinformatic workflows and documenting code with version control.
- Experience working with large-scale oncology datasets (e.g., TCGA, CPTAC, DepMap, PRISM)
- Understanding of statistical methods, machine learning and/or AIML algorithms, and survival analysis techniques.
Benefits
- Professional development opportunities
- Flexible working hours
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard skills
Computational BiologyBioinformaticsCancer BiologyPythonRStatistical methodsMachine learningAI algorithmsSurvival analysisBioinformatic workflows
Soft skills
LeadershipTeam buildingScientific expertiseAnalytical skillsCommunicationCollaborationInfluencingCritical evaluationStrategic thinkingMultidisciplinary coordination
Certifications
PhDMS