Principal Scientist, Bioinformatics Human Genetics at F. Hoffmann-La Roche Ltd - Poland

South San Francisco, California, United States -

Full Time

Start Date

Immediate

Expiry Date

21 Jun, 26

Salary

279900.0

Posted On

23 Mar, 26

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Statistical Genetics, Bioinformatics, AI/ML, Sequence-to-Phenotype Research, Target Discovery, Neuroscience, GWAS, Fine-mapping, Colocalization, Rare-Variant Association, Multiomics Integration, Single-Cell Genomics, Machine Learning, Python, R, Workflow Management

Industry

Biotechnology Research

Description

A healthier future. It’s what drives us to innovate. To continuously advance science and ensure everyone has access to the healthcare they need today and for generations to come. Creating a world where we all have more time with the people we love. That’s what makes us Genentech. Genentech’s Department of Human Genetics sits at the center of our precision medicine strategy. We combine large-scale human 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. THE 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. In this role, you will: 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 (e.g., gene-based burden and variance-component approaches), 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—and critically, 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. Example focus areas you may lead include: Building a neuroscience-focused variant interpretation capability that links coding and noncoding variants to molecular function and disease-relevant phenotypes, with confidence scoring and integration into downstream genetics workflows. Multiomic-first locus interpretation from fine-mapped signals to causal genes, cell states, and regulatory mechanisms Structure-guided rare-variant discovery and interpretation frameworks Genetics-informed patient stratification and biomarker development using deep phenotyping and clinically relevant outcomes WHO YOU ARE 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 Demonstrated track record developing and/or 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 (helpful but not required) Relocation benefits are available for this job posting. 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. Benefits 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, discrimination on the basis of Protected Veteran status, individuals with disabilities status, and consistent with all federal, state, or local laws. If you have a disability and need an accommodation in relation to the online application process, please contact us by completing this form Accommodations for Applicants. Who We Are Genentech, a member of the Roche group and founder of the biotechnology industry, is dedicated to pursuing groundbreaking science to discover and develop medicines for people with serious and life-threatening diseases. To solve the world's most complex health challenges, we ask bigger questions that challenge our industry and the boundaries of science to transform society. Our transformational discoveries include the first targeted antibody for cancer and the first medicine for primary progressive multiple sclerosis. The next step is yours. To apply today, click on the "Apply for this job" button. 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, discrimination on the basis of Protected Veteran status, individuals with disabilities status, and consistent with all federal, state, or local laws. If you have a disability and need an accommodation in relation to the online application process, please contact us by completing this form Accommodations for Applicants.

Responsibilities

This role involves leading end-to-end human genetics analyses, integrating genetics with functional and multimodal genomics data to identify causal genes and advance AI/ML applications for genomics research. The scientist will also be responsible for building scalable software, providing scientific leadership, and translating model outputs into actionable insights for therapeutic decisions.