Principal Scientist, Bioinformatics Human Genetics

Overview A healthier future drives us to innovate. We continuously advance science to ensure everyone has access to the healthcare they need now and for generations to come. Genentech’s Department of Human Genetics sits at the center of our precision medicine strategy, combining large-scale genetic evidence with rich molecular, cellular, and clinical data to uncover causal disease biology and translate it into actionable targets, biomarkers, and patient stratification strategies. Opportunity We are seeking a Principal Scientist (Bioinformatics track) to lead cutting‑edge statistical genetics and AI/ML‑enabled ‘sequence-to-phenotype’ research that directly accelerates target discovery and translation—particularly in Neuroscience (e.g., Multiple Sclerosis, Parkinson’s disease, ALS). This is a methods‑forward role for a scientist who thrives at the interface of human genetics, multimodal genomics, and machine learning, and who wants to see their work influence real therapeutic decisions. Responsibilities Lead end‑to‑end human genetics analyses across array and sequencing cohorts, including rigorous QC, GWAS, fine‑mapping, colocalization, gene prioritization, and rare‑variant association, with attention to multi‑ancestry and bias/robustness. Integrate genetics with functional and multimodal genomics, including single‑cell and multiome (RNA/ATAC), molecular QTLs, and perturbation datasets to identify causal genes, implicated cell types/states, and mechanistic hypotheses. Advance and operationalize AI/ML for genomics by evaluating and adapting modern sequence‑to‑function approaches for coding and noncoding variation, establishing reliability gating (calibration, uncertainty quantification, and robust benchmarking) so model outputs are decision‑ready rather than purely exploratory. Translate model outputs into genetics‑ready quantities (e.g., fine‑mapping priors, rare‑variant weights, mechanism‑linked scores) that improve discovery power and interpretability while enabling systematic, repeatable locus‑to‑biology workflows. Anchor evaluation in experimental and real‑world evidence, leveraging perturbation ground truth (e.g., reporter assays, CRISPR/base‑editing studies, multiplex functional assays) and genetics/omics benchmarks to assess generalizability across cell states and datasets. Build scalable, reusable software and workflows that make advanced genetics and AI methods broadly accessible—prioritizing reproducibility, documentation, and production‑quality standards. Provide scientific leadership and mentorship, partnering closely with cross‑functional computational and experimental teams to shape strategy, communicate results clearly, and drive high‑impact deliverables that inform research and development decisions. Maintain scientific excellence through publications, conference presentations, and contributions to strategic external collaborations. Lead example focus areas such as building a neuroscience‑focused variant interpretation capability, multi‑omic‑first locus interpretation, structure‑guided rare‑variant discovery frameworks, and genetics‑informed patient stratification and biomarker development. Qualifications PhD in Statistical Genetics, Computational Biology, Bioinformatics, Biostatistics, Machine Learning, Computer Science, or a related quantitative field, followed by postdoctoral experience. Demonstrated scientific impact through high‑quality peer‑reviewed publications and conference presentations, with evidence of intellectual leadership (e.g., first/corresponding author). Deep expertise in human/statistical genetics spanning common‑ and rare‑variant methods, study design, and rigorous inference. Strong statistical and programming skills (e.g., Python and/or R) and commitment to reproducible science (version control, testing, workflow management, documentation). Experience working with large‑scale genomics datasets and modern compute environments (cloud and/or HPC). Proven ability to lead complex, ambiguous scientific problems end‑to‑end and deliver robust solutions. Excellent communication and collaboration skills with the ability to influence across disciplines. Preferred Track record developing and scaling reusable analytics pipelines, platforms, or libraries used by multiple teams. Strong experience integrating genetics with single‑cell/multiome data, molecular QTLs, functional genomics, and perturbation evidence. Applied experience with ML for genomics, including benchmarking, calibration/uncertainty, model reliability, and transfer/generalization assessment. Interest or prior experience in neuroscience, neuroimmunology, or neurodegeneration. Compensation and Benefits The expected salary range for this position based on the primary location of California is $150,700 to $279,900. Actual pay will be determined based on experience, qualifications, geographic location, and other job‑related factors permitted by law. A discretionary annual bonus may be available based on individual and company performance. This position also qualifies for the benefits detailed at the link provided below. Relocation benefits are available for this job posting. Equal Opportunity Genentech is an equal opportunity employer. It is our policy and practice to employ, promote, and otherwise treat any and all employees and applicants on the basis of merit, qualifications, and competence. The company's policy prohibits unlawful discrimination, including but not limited to protection of Veteran status, individuals with disabilities status, and is consistent with all federal, state, or local laws. #J-18808-Ljbffr F. Hoffmann-La Roche AG