Senior Director, AI Diagnostics

Accountabilities: Build and lead the team: Grow the existing algorithm research organization, recruit key talent, develop technical leaders and people managers, and establish high standards for scientific rigor, collaboration, and execution. Own research-to-launch development: Define development milestones and evidence-based go/no-go criteria across feasibility, validation, and launch, ensuring clear ownership, reproducible analyses, and well-documented decisions. Set strategy with cross-functional partners: Collaborate with Product, Clinical, and Commercial leaders to prioritize clinical use cases, define intended use and success criteria, and make investment, continuation, and discontinuation decisions based on scientific evidence, technical feasibility, and clinical and business value. Advance foundation model and multimodal AI capabilities: Establish and guide the foundation model research strategy, identify opportunities across longitudinal molecular, imaging, and clinical data, and translate advances in foundation models and multimodal deep learning into differentiated diagnostic products. Ensure clinical and analytical quality: Oversee the development of algorithms using reliable, well-characterized data and rigorous validation methods appropriate for regulated diagnostics. Lead deployment and lifecycle oversight: Partner with Engineering and Quality to ensure reproducible implementation, model and data versioning, controlled releases, and ongoing post-launch performance monitoring. Drive scientific and organizational influence: Establish development processes that teams consistently adopt and influence senior stakeholders across R&D, Quality, Regulatory, Clinical, and Commercial functions, including where there is no direct reporting relationship. Requirements Ph.D. in Bioinformatics, Computational Biology, Machine Learning, Biostatistics, or a related quantitative discipline. A Master's degree combined with substantial additional relevant experience will also be considered. Significant 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 level are more important than a previous job title. Proven ability to define and advance a foundation model research strategy, supported by deep knowledge of representation learning, pretraining, adaptation, and model evaluation. Ability to articulate an original, clinically grounded scientific 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 technical and strategic decisions, validation responsibilities, cross-functional contributions, and post-launch results. Strong understanding of analytical and clinical validation for regulated diagnostics, including regulatory expectations for AI-based products. Deep expertise in at least one oncology data modality, such as next-generation sequencing (NGS), liquid biopsy, transcriptomics, or whole-slide imaging, together with sufficient breadth to lead work across multiple modalities. Ability to critically evaluate model design, data assumptions, evaluation methods, and Python-based analyses through technical reviews. Routine coding is not a core responsibility. Experience managing people managers, developing technical leaders, and building effective development processes that can be adopted across teams. Strong stakeholder management and communication skills, with the ability to influence senior leaders across R&D, Quality, Regulatory, Clinical, and Commercial functions without relying on 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 Experience developing multimodal models that combine at least two data types, such as genomics, imaging, and clinical data. Scientific and technical contributions demonstrated through deployed products, patents, publications, or substantial research collaborations. Experience evaluating or co-developing algorithms with academic institutions, pharmaceutical companies, or technology partners. Experience building or leading the adoption of AI infrastructure, including MLOps capabilities, model registries, or large-scale model training environments. Experience working with real-world data, including claims, electronic health records (EHR), or registry data linked to clinical outcomes. Benefits Annual salary range of $259,400–$324,200 USD, with actual compensation determined by factors such as skills, experience, certifications, and work location. Comprehensive medical, dental, and vision coverage for eligible employees and their dependents. Life and disability insurance plans. Complimentary testing for employees and their immediate families. Fertility care benefits. Pregnancy and baby-bonding leave. 401(k) benefits. Commuter benefits. Employee referral program. Remote work opportunity within the United States.