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We are looking for a Principal Staff Scientist (computational) to provide analytical leadership in Artificial Intelligence/ Machine Learning (AI/ML) and genomic data analysis across the Human Genetics Programme.
About the Programme:
The Human Genetics Programme brings together large-scale human cohorts, deep molecular profiling and linked patient records to build robust, clinically useful models of human disease. Our goal is to generate the datasets and models that explain who develops disease, when and why, and to turn that understanding into better prediction, prevention and treatment across diverse populations.
We are building clinic-anchored cohorts that are explicitly designed for prediction and causal inference. Working with healthcare partners, we recruit patients in real-world clinical settings, collect repeat samples from relevant tissues, and link these to rich outcomes data. Across these cohorts we generate multiomic data at scale, using broadly deployable assays such as serum proteomics, metabolomics and transcriptomics, alongside whole-genome sequencing.
Faculty groups in the Programme work together on shared, centrally generated cohorts and datasets. These datasets have a common structure: repeat molecular measurements linked to clinical outcomes over time. That means groups face the same analytical challenges, including learning from sparse, high-dimensional and time-resolved data, separating causal drivers from downstream correlates, and building models that hold up in new populations and healthcare settings. AI and ML methods are well placed to address these challenges. The Programme already has considerable analytical and statistical strength across its groups, including colleagues extending their work into AI/ML. We are now looking for a Principal Staff Scientist to lead and coordinate that effort, and to set the direction for how AI/ML develops across the Programme.
What you'll be doing:
You will provide strategic direction and oversight for AI/ML across the Programme, working with the statisticians, computational scientists and analysts already in our faculty groups, and contributing hands-on to the analysis yourself. This spans predictive modelling and causal inference, and the full range of our data, from genetic and functional genomic data through to longitudinal multiomic and clinical data from patients.
You will shape the Programme's analytical strategy and lead on how models are built and validated for clinical use. A central part of the role is analytical consistency: methods, tools and standards developed for one project should be available to the rest, so that expertise built in one group benefits everyone.
Most of your hands-on work will be on projects in the Anderson group, which studies inflammatory bowel disease. Open IBD is an inception cohort of 2,000 patients, recruited prior to diagnosis and sampled repeatedly from that point, generating scRNA-seq of gut biopsies, whole-genome sequencing and stool microbiome data alongside linked clinical records. This gives molecular profiles from before diagnosis and treatment, and disease trajectories that can be modelled from onset. IBD-Response is a multi-centre study of treatment response in IBD, combining scRNA-seq of PBMCs, serum proteomics and stool microbiome sequencing with genetic data to predict which patients will benefit from which therapy.
This is a senior appointment. You will be the lead intellectual driver for the Programme's AI/ML strategy, sit on the Programme Working Group, contribute to strategic decisions, and represent the Programme in this area within Sanger and externally.