Senior Data Scientist at Edwards Lifesciences

Compensation

$108,000 – $153,000/yr

Setup: Hybrid
Location: Irvine, California
Type: Full-time
Level: Senior

Description

Edwards Lifesciences is seeking a Senior Data Scientist to join their Implantable Heart Failure Management (IHFM) AI, Product and Platforms organization. The role involves owning modeling for clinical or product domains, driving problem framing with product and clinical partners, ensuring offline-validation rigor, and authoring model documentation meeting FDA submission expectations. Based in Irvine, CA, the candidate will collaborate in-person with cross-functional teams to shape patient-focused technology.

Required Skills

Python
SQL
PyTorch
TensorFlow
MLflow
scikit-survival
Bayesian modeling
Causal inference
Generative approaches
Multimodal modeling

Additional Information

Minimum Education: Bachelor's in related field (Computer Science, Engineering, Biostatistics or Scientific) plus 4 years of experience, or Master's plus 3 years of experience.
Preferred Qualifications: Depth in at least one data modality relevant to IHFM, for example, time-series, or medical imaging (echocardiography, cardiac magnetic resonance imaging, or computed tomography).
Command of the relevant architecture families and of transfer and self-supervised learning.
Strong Python and SQL, and depth in modeling and experiment tracking such as PyTorch, TensorFlow, and MLflow.
Command of evaluation, calibration, and clinical performance methodology, including subgroup analysis, and the ability to author documentation suitable for regulatory submission.
A track record of owning modeling for a domain and communicating results to clinical, product, and regulatory partners.
Experience with clinical validation, retrospective clinical data, or regulated medical software (SaMD).
Depth in time-series or medical imaging modeling, including segmentation and registration, for example, MONAI, pydicom, or SimpleITK.
Bayesian modeling, causal inference (DoWhy, EconML), or survival analysis (lifelines, scikit-survival).
Generative approaches for augmentation or synthetic data (autoencoders, GANs, or diffusion models).
Exposure to multimodal modeling, combining signals, imaging, labs, and notes into patient-state models.
Familiarity with R, JAX, or hyperparameter optimization (Optuna, Ray Tune).
Contributions to internal or external research (publications, patents, or venues such as MICCAI or ML4H).