Sr. Scientist, Clinical Bioinformatics

10x Genomics•Pleasanton, CA

About The Position

About The Role 10x Genomics is establishing a diagnostics effort, translating our leading single-cell and spatial assay technologies into impactful clinical applications. We are seeking a Sr. Scientist to join the clinical bioinformatics team. The ideal candidate excels at distilling complex biological questions into actionable computational strategies, implementing computational/statistical methods and applying them to large-scale single-cell or spatial transcriptomics datasets to derive clinically meaningful insights. The role requires a biology-first mindset, proficiency with large-scale bioinformatics analyses, strong scientific acumen and statistical rigor. The successful candidate will have an opportunity to work with some of the largest biomedical datasets assayed using cutting-edge 10x Genomics technologies, deriving clinical insights that power the next generation of clinical diagnostics. What You Will Be Doing:

Requirements

Ph.D. in bioinformatics, computational biology, genomics or a related discipline with extensive hands-on experience in single-cell NGS data analysis.
A minimum of 5 years of industry experience post Ph.D.
Experience analyzing large-scale single-cell or spatial transcriptomics datasets to derive biologically meaningful insights and/or diagnostic biomarkers.
In-depth understanding of the assumptions, limitations and caveats of statistical methods.
Experience developing and optimizing high-performance, scalable code.
Proficiency working in a Linux environment.
Goal-oriented, self-motivated and an independent problem solver.
Meticulous attention to detail and a conscientious work ethic.

Nice To Haves

Hands-on experience with 10x Genomics single-cell and in-situ transcriptomics technologies is a strong preference
Hands-on research experience in cancer or autoimmune diseases is a strong preference
Knowledge of clinical genomics, biomarker discovery and diagnostics
Development of statistical models and algorithms for single-cell or spatial transcriptomics data
Application of machine learning, particularly in the context of genomics
Proficiency with workflow orchestration frameworks such as Snakemake, Nextflow or Martian
Programming best practices including data analysis reproducibility, version control, design patterns, testing, debugging and profiling
Track record of writing production-level code or maintaining published software packages
High-throughput computing infrastructure such as HPCs or cloud computing

Responsibilities

Implement rigorous computational/statistical methods for single-cell and spatial transcriptomics data analysis.
Derive actionable insights from clinical/translational single-cell or in-situ spatial datasets.
Design, implement and validate biomarkers for diagnostic applications.
Implement and maintain bioinformatics pipelines for reproducible, large-scale data processing.
Process and analyze single-cell or in-situ spatial transcriptomics datasets spanning hundreds to thousands of samples.