Lead Bioinformatician (cfDNA Algorithms and Pipelines)

Natera

1d•$130,900 - $163,600•Remote

About The Position

Natera is seeking a Lead Bioinformatician to advance the algorithmic foundations of our diagnostic assays supporting Women’s and Organ health. This is an individual contributor role. You will bring the genomics expertise the team needs to pull reliable signals out of sequencing data that is often ambiguous. You will build the methodological foundations and implement the algorithms needed for detecting variants (SNVs, Indels, CNVs, SVs) that are hard to call accurately in low fraction (fetal, donor cfDNA) samples. The ideal candidate will have deep experience in algorithmic genomics, strong programming skills, and a passion for developing scalable, clinically impactful computational tools.

Requirements

Degree in Bioinformatics, Computational Biology, Bioinformatics, Human Genetics, or a related field. We do not require a Ph.D. or M.S.: equivalent depth built through work counts fully.
4+ years analyzing short-read sequencing data for screening or diagnostic applications, preferably in a regulated, accredited, or production-adjacent setting. We count relevant experience from the point your work became substantially independent, however you got there.
Experience contributing to the bioinformatics workflows behind a sequencing assay or panel.
Experience seeing a complex investigation through to resolution across biological, analytical, and systems-level causes.
Experience developing, validating, or benchmarking bioinformatics methods (e.g. SNVs, CNVs, SVs), particularly for analytically difficult regions.
Proficient in Python with demonstrated experience prototyping bioinformatics tools or callers.
Enough human genetics depth to contribute to the panel design: variant spectrum, population frequency, and where compromises on accuracy can (and cannot) be made.
Familiarity with identifying regions where short reads cannot be placed with confidence: homologous sequence and pseudogenes, low-complexity and repeat structure, and copy-number-heavy regions.
Experience contributing to study designs or performance criteria for a bioinformatics analysis method.

Nice To Haves

Direct non-invasive prenatal, reproductive, or carrier screening experience.
Experience with cfDNA, or with another application where the molecules you care about are a small minority of what was sequenced.
Experience developing algorithms for both short-read and long-read sequencing platforms (e.g., Illumina, ONT, PacBio).
Cross-functional credibility with genetic counseling, variant management, reporting, and production-adjacent groups.
Experience in an accredited or high-complexity laboratory, or familiarity with production support or escalation processes.

Responsibilities

Provide genomics algorithm insights to our expanded panel roadmap, including which genes are worth considering and why, working with Product, the Laboratory Directors, and Research, who own that decision jointly.
Help define the approach for analytically difficult genes and assay edge cases, and weigh what is scientifically defensible against what is technically possible.
Define the computational strategy for new targeted and special-purpose callers.
Help decide when a new caller is justified and when an existing method should be extended instead.
Prototype and benchmark new methods, and work with the engineering team to get methods into production.
Serve as a genomics consultant on complex production escalations, separating biological causes from analytical and pipeline ones.
Recognize when a result looks suspicious because of where the reads came from and not because the analysis went wrong.
Turn one-off investigations into durable rules and design changes that reduce repeat work.
Act as bioinformatics liaison with Variant Management, Reporting, and Laboratory Operations on data quality, variant representation, and system integration.
Provide the scientific rationale that the accountable Quality and Laboratory functions rely on when they decide a method is ready to deploy.
Help define what correct looks like for AI-assisted scientific investigation, for example what an agent may and may not conclude from a region-level finding without a human signing off.