Natera is hiring a Senior Director to lead the research, development and clinical translation of AI and computational algorithms for oncology diagnostics, from research prioritization through validation, deployment, and ongoing performance monitoring. Natera’s longitudinal oncology data resources span tumor genomics, ctDNA, transcriptomics, pathology images, and clinical outcomes, creating opportunities to develop and validate models across molecular, imaging, and clinical data. This role turns that data into multimodal AI products that change how oncologists treat patients.

We are looking for a scientific leader with a compelling vision for how foundation models can advance oncology diagnostics. You will shape our foundation model strategy - identifying the highest-value opportunities in longitudinal molecular, imaging, and clinical data, defining the capabilities we need to build, and guiding their translation into clinically useful products. This requires both the imagination to pursue new possibilities and the judgment to establish where these approaches deliver meaningful advantages. You will lead an established algorithm team, develop its technical and leadership capabilities, and recruit talent aligned with the product roadmap. With Product and other partners, you will set strategy and decide which opportunities move toward clinical deployment.

Key Responsibilities: Build and lead the team: Grow the existing algorithm research team, hire key talent, and set a high bar for scientific rigor and execution.

Own the path from research to launch: Establish development milestones and evidence-based go/no-go criteria from feasibility through validation and launch, with clear ownership, reproducible analyses, and documented decisions.

Set strategy with Product: Partner with Product, Clinical, and Commercial leaders to prioritize clinical use cases, define intended use and success criteria, and make investment, continuation, and stop decisions based on evidence, feasibility, and clinical and business value.

Push the technical frontier: Bring in foundation models and multimodal deep learning, and turn our data into products that set us apart from competitors.

Deliver with quality: Make sure every algorithm is built on clean data, validated rigorously, and monitored after launch, so it holds up for clinicians, patients, and regulators.

Qualifications:

• Ph.D. in Bioinformatics, Computational Biology, Machine Learning, Biostatistics, or a related quantitative field. A Master’s degree with substantial additional experience will be considered.
• Substantial experience developing computational products, including sustained leadership of multidisciplinary teams and ownership of complex development programs through clinical deployment. Demonstrated scope and impact equivalent to a Senior Director are more important than prior title.
• Demonstrated ability to define and advance a foundation model research strategy, grounded in a deep understanding of representation learning, pretraining, adaptation, and evaluation. Able to articulate an original, clinically grounded vision, translate it into a research roadmap, and guide teams through consequential scientific and technical decisions.
• Demonstrated accountability for taking at least one AI/ML or computational algorithm from research through validation and clinical deployment within a diagnostic product. Candidates should be able to explain their decisions, validation responsibilities, cross-functional role, and post-launch results.
• Strong understanding of analytical and clinical validation for regulated diagnostics, including what regulators expect from AI-based products.
• Depth in at least one oncology data modality, such as NGS, liquid biopsy, transcriptomics, or whole-slide imaging, and sufficient breadth to lead across modalities. Able to challenge model design, data assumptions, evaluation methods, and Python-based analyses through technical reviews; routine coding is not a core responsibility.
• Demonstrated experience managing people managers and developing technical leaders, building development processes that teams adopt, and influencing senior stakeholders across R&D, Quality, Regulatory, Clinical, and Commercial without direct authority.
• Experience overseeing algorithm deployment and lifecycle management, including reproducible implementation, model and data versioning, release controls, and post-launch performance monitoring, in partnership with Engineering and Quality.

Preferred Qualifications:

• Multimodal experience: Experience building models that combine two or more data types, such as genomics, imaging, and clinical data.
• Scientific and technical contribution: Evidence of contribution through deployed products, patents, publications, or substantive collaborations.
• External partnerships: Experience evaluating or co-developing algorithms with academic groups, pharma, or tech partners.
• AI infrastructure: Experience building or leading the adoption of MLOps, model registries, or large-scale training environments.
• Real-world data: Experience working with claims, EHR, or registry data linked to outcomes.