Associate Director of Bioinformatics (Women's Health and Organ Health)

Natera is seeking an Associate Director to lead a team of Bioinformatics Scientists in our Women's Health and Organ Health research organization. You will oversee the scientific planning and execution of research and assay-development of diagnostic projects, and you will be responsible for creating production-ready pipeline components. You will manage your team and its professional growth, the research strategy behind new product development, and the handoff of research work into production. 

The role calls for deep experience in algorithm and assay development, NGS data processing across multiple modalities, and the full life cycle of diagnostic product research and development in a regulated (CLIA) setting. You will evaluate new technologies and NGS assays, implement methods to optimize performance, and help define the product profile from a bioinformatics perspective, with input from R&D, Product, and Laboratory Directors.

Primary Responsibilities:

Team Leadership and Development: Lead and mentor a team of Bioinformatics Scientists. Own their professional growth, their responsibilities, and the standard the team holds itself to.

Research Strategy: Own the research strategy behind new product development, from scoping through execution, working with cross-functional stakeholders on what the team takes on and in what order.

Assay Science: Lead bioinformatics analysis for assay development and optimization, and troubleshoot experiments alongside laboratory scientists. This spans multi-omic approaches including methylation and fragmentomics and other cell-free DNA (cfDNA) derived features. It also spans short-read and long-read sequencing and both hybrid-capture and amplicon target enrichment.

Study Design and Data Quality: Partner with laboratory teams on study design at the research and feasibility stage, covering new technology assessment, optimization, and performance determination. Make sure the resulting data holds up before anyone builds on it.

Analysis Method Improvement: Advise and prototype improvements to analysis pipelines, including variant detection, quality control, and modality-specific processing: methylation calling, fragment-size and end-motif analysis, error suppression for deep targeted panels, and structural-variant calling.

Production Readiness and Handoff: Move research prototypes into stable production workflows. Hold the team to software engineering practice, including version control, testing, continuous integration and delivery, and containerization. Automate the routine parts of research data management, pipeline execution, and reporting so the team's time goes to science.

Ways of Working: AI is a routine part of the work here. As the leader of this team you set what correct use looks like: what an agent may conclude on its own, and what requires a scientist to sign off. We do not screen for prior experience with these tools, and many strong candidates come from environments where they were restricted; we provide the tooling and the ramp time.

Cross-functional Partnership: Work with data science, molecular biology, pipeline engineering, biostatistics, quality assurance, and laboratory operations on new products and on the transition of research work into production. Act as the subject-matter expert those groups come to, and explain findings and the roadmap clearly at every level of the company.

What success looks like after a year:

• The team has a research roadmap you own, and progress against it is visible outside the team.
• You independently advise and guide research strategies in cross functional settings.
• New analysis methods from your team are implemented within production code and demonstrate expected performance on benchmark studies..
• You and team members become trusted experts that other functions direct their hard questions to, and can independently support decision making.
• AI agent-assisted work is normal on the team, with a standard for it that you set.

Qualifications:

• Ph.D. in Bioinformatics, Computational Biology, Computer Science, Mathematics, Engineering, Biostatistics, or a related field. (An M.S. with equivalent experience considered).
• 7+ years in bioinformatics, including 3+ years leading scientists and working across functions.
• Demonstrated ownership of a team that took scientific research into real clinical use, not only to a result.
• Demonstrated experience developing or scientifically guiding diagnostic sequencing assays.

Knowledge, Skills, and Abilities:

What we are screening for

• Deep expertise in next-generation sequencing analysis: hybrid-capture or amplicon-based target enrichment designs, sequencing quality control, secondary analysis, and variant calling.
• Experience with cell-free DNA sequencing and other omics data, especially in a diagnostics setting. Preferred to have some exposure to multiomics types : methylation, fragmentomics, or related cfDNA signals.
• Working  knowledge of how a diagnostic product moves through development in a regulated, accredited setting, including the practices and standards that apply.
• Strong Python, programming, and data analysis skills.
• Fluency in exploratory data analysis and visualization on complex data sets, and the ability to translate findings into actionable recommendations.
• Understanding of sequencing workflows from sample extraction through the instrument, deep enough to tell a biology problem from an analysis problem.
• A demonstrated record of assessing a new platform, assay chemistry or method against established benchmarks..
• Clear communication of technical detail to people who do not share your background.
• The ability to run several objectives and timelines at once without close supervision.

Strong candidates may also have

• Long-read sequencing (PacBio HiFi, Oxford Nanopore) in addition to short-read (Illumina).
• R, shell scripting, or Java.
• Cloud computing (AWS, GCP) and workflow orchestration (Snakemake, WDL, Nextflow, Cromwell).
• Containerization (Docker, Kubernetes) for reproducible, production-grade analyses.
• A record of conference presentations or peer-reviewed publication.
• A desire to work in a fast-paced environment where a small team has high impact.