Staff Data Scientist

Edwards Lifesciences • Irvine, CA

1d • $126,000 - $178,000 • Onsite

About The Position

Many structural heart patients suffer from heart failure with limited options. Our Implantable Heart Failure Management (IHFM) team, part of the AI, Product and Platforms organization, is at the forefront of addressing these unmet patient needs through pioneering technology that enables early, targeted therapeutic intervention. Our innovative solutions are not just transforming patient care but also creating a unique and exciting environment for our team members. It is our driving force to help patients live longer and healthier lives. Join us and be part of our inspiring journey. At Edwards Lifesciences, the Implantable Heart Failure Management (IHFM) AI, Product and Platforms organization designs and builds the software and data products that clinicians and patients depend on. As a Staff Data Scientist, you design the modeling and evaluation approaches that others build on for a problem class, set validation strategy, and raise the modeling rigor of the teams around you, while remaining deeply hands-on. Based in Irvine, CA, you'll join a high-impact medtech innovation hub in the heart of Orange County, collaborating in person with cross-functional teams to shape patient-focused technology.

Requirements

• Bachelor's in Computer Science, Engineering, Biostatistics or Scientific field plus 6 years of experience including either industry or industry / education -or- Master's plus 5 years -or- PhD plus 2 years.
• Only candidates within a 50-mile radius of Irvine, California will be considered.

Nice To Haves

• Deep domain expertise in cardiac, hemodynamic, imaging, or physiological modeling.
• Recognized depth across multiple paradigms and architecture families, including self-supervised and multimodal approaches.
• Depth in one or more data modalities (physiological modeling, medical imaging, or multimodal fusion) and in calibration and uncertainty quantification.
• The ability to set modeling and validation standards that others follow, and to represent modeling decisions to clinical and regulatory partners.
• Fluency across the modeling stack (for example, PyTorch, PyTorch Lightning, and experiment tracking) sufficient to set team practice.
• Advanced generative or graph methods (diffusion models, graph neural networks), or federated learning methodology.
• Demonstrated technical leadership and mentorship.
• Performance engineering for training (C++, CUDA, JAX, or DeepSpeed).
• A history of shaping clinical validation strategy and regulatory submissions for models.
• Recognized research contributions in AI/ML for health.
• Active learning or weak supervision to reduce clinical labeling cost.

Responsibilities

• Design the modeling and evaluation approaches others build on for a problem class, for example, medical imaging segmentation or multimodal patient-state modeling, and build the reference implementations yourself.
• Bring advanced paradigms to bear where they fit, including self-supervised and contrastive learning, multi-task learning, and cross-modal fusion.
• Set offline validation, calibration, and clinical performance evaluation strategy, including subgroup and fairness analysis, with Medical Affairs and Clinical Science.
• Establish reusable modeling patterns, evaluation harnesses, and documentation practices that meet regulatory submission expectations.
• Apply label-efficient methods (active, semi-supervised, weak supervision) and efficient training at scale (distributed training with Ray or PyTorch).
• Lead evaluation of emerging AI/ML for health research and decide what to adopt for IHFM problems.
• Mentor senior and mid-level data scientists and shape the research to productization handoff with AI/ML Engineers (Applied).
• Guide implementation of processes and tools to develop, analyze, and improve model performance and data accuracy; apply predictive modeling and algorithms to datasets to optimize outcomes (e.g., clinical trial experience) and ensure models remain current.
• Partner with internal and external stakeholders to plan implementation, testing, training, and monitoring of machine learning models; conduct ad hoc analyses (e.g., effectiveness metrics) and present results and insights to leadership.
• Drive processing of structured and unstructured data with data stewards, including data governance practices; identify and integrate diverse data sources to enhance solutions in collaboration with business stakeholders.

Benefits

• competitive salaries
• performance-based incentives
• a wide variety of benefits programs to address the diverse individual needs of our employees and their families.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior