Senior Computational Scientist - Systems Biology & Next-Gen Data Engineering

Step into a role where code meets clinical impact. We’re building the next generation of data-driven diagnostics - at the intersection of biology, computation, and machine learning. You’ll join a tight-knit team of scientists and engineers decoding huge multi-omic datasets to uncover patterns that actually change patient outcomes. What You’ll Do Architect and optimize computational pipelines that turn raw high-resolution molecular data into clean, interpretable insights. Apply advanced statistical and algorithmic frameworks to analyze ultra-large cell-level and spatial datasets. Design and validate novel biomarkers and molecular signatures that accelerate diagnostic innovation. Maintain scalable, reproducible data workflows capable of handling cohorts of hundreds or thousands of biological samples. Partner with biologists, data scientists, and software engineers to push new ideas from concept to clinical utility. RequirementsWhat You Bring Ph.D. (or equivalent experience) in computational biology, bioinformatics, genomics, or a quantitative discipline. 5+ years working hands-on with single-cell, spatial, or high-throughput sequencing data. Deep understanding of algorithmic performance, statistical rigor, and how analytical assumptions translate to biological meaning. Strong engineering mindset - you write clean, extensible code that scales gracefully. Expert-level fluency in Linux and modern programming ecosystems. Independent drive, intellectual curiosity, and relentless attention to detail. Bonus Points For Experience with next-generation spatial or single-cell assay platforms. Background in oncology, immunology, or systems-level disease research. Familiarity with biomarker discovery pipelines or clinical data integration. Machine learning or statistical modeling applied to multi-omic data. Practical experience with workflow orchestration (Nextflow, Snakemake, or similar). Solid software engineering habits - reproducible analysis, version control, peer review, and testing. Comfort operating in high-performance or cloud computing environments. If you’re driven by the idea of building tools that help decode biology at scale and you thrive where data meets diagnostics, this is your stage.

• Ph.D. (or equivalent experience) in computational biology, bioinformatics, genomics, or a quantitative discipline.
• 5+ years working hands-on with single-cell, spatial, or high-throughput sequencing data.
• Deep understanding of algorithmic performance, statistical rigor, and how analytical assumptions translate to biological meaning.
• Strong engineering mindset - you write clean, extensible code that scales gracefully.
• Expert-level fluency in Linux and modern programming ecosystems.
• Independent drive, intellectual curiosity, and relentless attention to detail.

Bonus Points For

• Experience with next-generation spatial or single-cell assay platforms.
• Background in oncology, immunology, or systems-level disease research.
• Familiarity with biomarker discovery pipelines or clinical data integration.
• Machine learning or statistical modeling applied to multi-omic data.
• Practical experience with workflow orchestration (Nextflow, Snakemake, or similar).
• Solid software engineering habits - reproducible analysis, version control, peer review, and testing.
• Comfort operating in high-performance or cloud computing environments.

If you’re driven by the idea of building tools that help decode biology at scale and you thrive where data meets diagnostics, this is your stage.