Bioinformatics Scientist III

Bio-Rad Laboratories•Pleasanton, CA

1d•Onsite

About The Position

Bio-Rad's Clinical Diagnostics Group is building the computational foundation for the next generation of molecular diagnostics, bringing together data across multiple assay modalities to support biomarker discovery and diagnostic development. This role owns the pipeline development and machine learning method design behind that effort. It sits within a team covering statistics, computational biology, data science, and software engineering, and is the primary technical owner for bioinformatics infrastructure and ML-driven discovery work. The role will be filled at either the Scientist or Senior Scientist level depending on the experience of the candidate, with the Senior level carrying broader ownership of technical direction and mentorship within the team.

Requirements

M.S. or Ph.D. in Bioinformatics, Computational Biology, Genomics, or a related technical field.
3+ years of hands-on omics data analysis (RNA-seq, ATAC-seq, proteomics, and/or ddPCR) for the Scientist level, or 5+ years including ownership of production analysis workflows and technical direction for the Senior level.
Demonstrated experience building production workflows in Nextflow or Snakemake with Docker or Singularity containerization, deployed on AWS (S3, EC2, Batch) or GCP (Dataproc, Cloud Storage).
Supervised and unsupervised methods plus practical deep learning experience on biological data, with exposure to multiomics integration and cross-modal feature alignment.
Fluency in Python (PyTorch, scikit-learn, pandas) and R (Bioconductor, Seurat, ggplot2), with strong engineering practice in version control, code review, testing, and documentation.

Responsibilities

Design and build reproducible, containerized analysis pipelines for high-dimensional biological data on cloud infrastructure, and establish the reproducibility and CI/CD standards the team builds against.
Develop machine learning methods for biomarker discovery, including supervised, unsupervised, and deep learning approaches applied to biological data.
Lead multi-omic integration work, aligning features across assay modalities into unified patient-level models.
Extend internal assay design tooling to support additional omic modalities and new application areas.
Contribute to the architecture of an in-house biomarker discovery capability, from candidate generation through feasibility assessment, in partnership with assay development, R&D, and product teams.