Lead Data Scientist (f/m/d)

Your new company This cutting-edge data science firm is driving transformation in life sciences through methodological excellence and innovation. Its research division is a hub for scientific exploration, where novel statistical techniques are developed to tackle some of the most pressing challenges in genomics and drug discovery. The organisation values intellectual curiosity, cross-disciplinary collaboration, and the pursuit of rigorous, reproducible science.They are looking for a Lead Data Scientist with a strong statistical methodology background to join their expanding team.
As Lead Data Scientist, you will be a driving force behind the creation of new statistical methodologies. Lead the development of original statistical models tailored to complex genomic data
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

This is a permanent role that can be fully home based from anywhere in the UK.
A PhD (or equivalent experience) in statistics, maths, physics, data science, computing, statistical genetics or a related field with a strong methodological focus
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)
You'll be joining a highly experienced team doing cutting-edge work to support drug discovery & development efforts at a wide range of pharmaceutical and biotech companies. 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