Lead Data Scientist - Drug Discovery

As Lead Data Scientist, you will be a driving force behind the creation of new statistical methodologies. You will:

• Lead the development of original statistical models tailored to complex genomic data

• Guide the integration of novel methods into pipelines

• Ensure methodological transparency and reproducibility across all research outputs

• Communicate the rationale and impact of new techniques to stakeholders and collaborators both internally and at clients

• Align scientific innovation with engineering and product development goals

• Work on projects to support drug discovery & development projects for a variety of clients within the pharmaceutical and biotech space

• Represent the organisation in academic and industry forums, showcasing methodological breakthroughs, Keywords: Statistical, Genetics, Bioinformatics, Genomics, Data, Scientist, Lead, Senior, GWAS, Polygenic, Risk, Score, Mendelian, Randomisation, Causal, Inference, Computational, Biology, Genetic, Epidemiology, Variant, Annotation, Pathway, Enrichment, Protein, Interaction, Networks, Biobank, Research, Modelling, Development Skills

• Bioinformatics

• Biology

• Data

• Enrichment

• Epidemiology

• Genetics

• Genomics

• Lead

• Pathway

• Risk

• Score

• Statistical

• Annotation

• Senior

• Scientist

• GWAS

• Genetic

• Computational

• Inference

• Polygenic

• Mendelian

• Randomisation

• Causal

• Variant

• A PhD (or equivalent experience) in statistics, maths, physics, data science, computing, statistical genetics or a related field with a strong methodological focus
• A track record of developing statistical models for genomic / biological research, preferably within a target identification or target validation setting
• Proven track record of innovation in statistical methodology, evidenced by publications, tools or project delivery
• Advanced coding skills in a language such as R or python and experience with statistical computing environments
• Deep expertise in methods such as GWAS, causal inference, polygenic risk scores, pathway analysis, Mendelian randomisation, etc
• Experience deploying methods in cloud-based infrastructures (AWS, Azure, GCP)
• The ability to communicate complex statistical ideas clearly