Deep Learning Data Scientist II

We are seeking a motivated Data Scientist to research, develop, and enhance AI/ML algorithms for computer vision and healthcare applications. This role involves developing and improving deep learning models, maintaining high standards of data quality, optimizing models for efficient deployment, and translating advances in AI research into practical healthcare solutions. The Data Scientist will collaborate with clinical partners and internal stakeholders to understand clinical and business needs and develop appropriate technical solutions. The ideal candidate will be capable of independently managing research and development projects while contributing effectively within cross-functional teams. Success in this role requires strong technical and communication skills, attention to detail, persistence, and a commitment to staying current with advances in AI/ML and computer vision., * Research, develop, evaluate, and enhance machine learning and deep learning algorithms for computer vision applications, translating advances in AI research into practical healthcare solutions.

• Implement and optimize model deployment pipelines to enable efficient, scalable, and hardware-optimized inference.
• Apply model optimization techniques to improve computational efficiency and inference performance while maintaining model accuracy.
• Maintain data quality and integrity throughout the data lifecycle to support robust algorithm development, training, and evaluation.
• Collaborate with clinical partners and internal stakeholders to understand clinical and business needs and translate them into appropriate AI/ML solutions.
• Independently manage assigned research and development projects from problem formulation through implementation, evaluation, and delivery.
• Ensure reproducibility of analyses and experiments through clear documentation, version-controlled workflows, and effective knowledge sharing.
• Stay current with advances in AI/ML, computer vision, and related technologies and evaluate their applicability to ongoing research and development.
• Communicate technical methods, results, and recommendations effectively to both technical and non-technical stakeholders.

• M.S. or Ph.D. in Computer Science, Electrical Engineering, Biomedical Engineering, Statistics, Applied Mathematics, or a related quantitative field.
• Minimum of two years of relevant research or professional experience in computer vision. For candidates with a master’s degree, the required experience must be professional experience obtained after completion of the degree.

Knowledge, Skills, and Abilities:

• Strong programming skills in Python and experience developing maintainable, reproducible code for machine learning applications.
• Strong foundation in state-of-the-art deep learning architectures (e.g., vision transformers) for computer vision tasks including image classification, object detection, and semantic segmentation.
• Proficiency with deep learning frameworks and libraries, including PyTorch and TensorFlow.
• Strong understanding of AI/ML evaluation metrics, benchmarking methodologies, and best practices for rigorous computer vision model validation.
• Solid understanding of model optimization techniques, including pruning, quantization, mixed-precision inference, and other strategies for improving computational efficiency and inference performance.
• Working knowledge of model deployment and inference tools, such as ONNX, LibTorch, and TensorRT.
• Self-driven and capable of independently navigating ambiguous research problems, working with incomplete data, and delivering high-quality results.
• Strong written and verbal communication skills, including the ability to prepare technical documentation, reports, and scientific manuscripts.

Preferred Qualifications:

• Experience working with multispectral and/or hyperspectral imaging data.
• Experience with generative AI models for medical image synthesis and simulation.
• Experience applying AI/ML methodologies to healthcare or biomedical applications, with demonstrated contributions to successful research, clinical, or product outcomes.