• Lead the development and execution of AI and data strategies that advance Verily’s mission to transform healthcare through data-driven innovation.

• Drive flagship AI and data science initiatives by designing scalable data architectures, building robust pipelines, and developing advanced machine learning and LLM-based solutions using complex clinical and real-world healthcare data.

• Partner cross-functionally with technical and business leaders to identify opportunities, deliver high-impact solutions, and enable data-informed decision-making across the organization.

• Mentor and inspire data scientists and engineers, fostering technical excellence, continuous learning, and best practices in AI, machine learning, and software development.

• Communicate complex analytical insights and technical outcomes effectively to both technical and non-technical stakeholders, translating data into actionable business and healthcare impact.

Qualifications

Minimum Qualifications

• Advanced degree in a quantitative field (e.g., Computer Science, Statistics, Applied Mathematics, Biomedical Informatics, Biomedical Engineering) or equivalent practical experience.

• 7+ years of experience applying advanced machine learning and AI techniques — including supervised/unsupervised learning, NLP, and LLMs — to clinical or digital health applications.

• Experience working with and curating real-world healthcare data (e.g., EHRs), including structured and unstructured clinical data, as well as direct experience with LLM technologies and prompt optimization.

• Strong proficiency in Python and experience building scalable data science and machine learning solutions.

Preferred Qualifications

• Ph.D. in a quantitative discipline with specialization in AI, machine learning, or related fields.

• Deep expertise in Agentic AI, LLMs, prompting, fine-tuning, retrieval-augmented generation (RAG), and transformer-based deep learning frameworks such as TensorFlow or PyTorch.

• Experience developing and deploying production-grade software and machine learning systems using modern software engineering best practices.

• Strong knowledge of clinical data, healthcare terminologies, and medical ontologies, with demonstrated success delivering complex cross-functional initiatives involving multiple stakeholders.

• Creative, methodical problem-solving skills with the ability to navigate ambiguity, develop hypotheses, evaluate options, and drive informed decisions.