Senior Computational Scientist - Systems Biology & NextGen Data Engineering

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Step into a role where code meets clinical impact. We're building the next generation of datadriven diagnostics - at the intersection of biology, computation, and machine learning. You'll join a tight\-knit team of scientists and engineers decoding huge multiomic datasets to uncover patterns that actually change patient outcomes.

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What You'll Do

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• Architect and optimize computational pipelines that turn raw highresolution molecular data into clean, interpretable insights.

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• Apply advanced statistical and algorithmic frameworks to analyze ultralarge celllevel and spatial datasets.

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• Design and validate novel biomarkers and molecular signatures that accelerate diagnostic innovation.

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• Maintain scalable, reproducible data workflows capable of handling cohorts of hundreds or thousands of biological samples.

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• Partner with biologists, data scientists, and software engineers to push new ideas from concept to clinical utility.

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Requirements<\/h3>
What You Bring

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• Ph.D. (or equivalent experience) in computational biology, bioinformatics, genomics, or a quantitative discipline.

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• 5+ years working handson with singlecell, spatial, or highthroughput sequencing data.

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• Deep understanding of algorithmic performance, statistical rigor, and how analytical assumptions translate to biological meaning.

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• Strong engineering mindset - you write clean, extensible code that scales gracefully.

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• Expertlevel fluency in Linux and modern programming ecosystems.

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• Independent drive, intellectual curiosity, and relentless attention to detail.

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Bonus Points For

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• Experience with nextgeneration spatial or singlecell assay platforms.

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• Background in oncology, immunology, or systemslevel disease research.

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• Familiarity with biomarker discovery pipelines or clinical data integration.

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• Machine learning or statistical modeling applied to multiomic data.

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• Practical experience with workflow orchestration (Nextflow, Snakemake, or similar).

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• Solid software engineering habits - reproducible analysis, version control, peer review, and testing.

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• Comfort operating in highperformance or cloud computing environments.

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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.

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