Senior Staff Data Scientist

GE Healthcare GE Healthcare · Healthcare · Bengaluru, Karnātaka, India · Digital Technology / IT

Senior Staff Data Scientist at GE Healthcare focusing on advanced medical imaging and multimodal AI research for clinical deployment. The role involves defining research vision, leading development of state-of-the-art solutions using transformers and foundation models, ensuring robustness and evaluation, and translating research into clinically meaningful AI solutions. Requires PhD with 5-8+ years of post-PhD experience, strong publication record, and expertise in medical image analysis, PyTorch, multimodal learning, and MLOps in regulated environments.

What you'd actually do

  1. Define and own the research vision for Medical image analysis, Physical AI and Multimodal AI aligned with clinical and product needs.
  2. Lead development of state-of-the-art detection, segmentation, classification, and quantitative imaging solutions.
  3. Drive innovation using transformers, large vision and multimodal models, self-supervised and weakly supervised learning.
  4. Establish high standards for robustness, generalization, uncertainty handling, and evaluation in clinical settings.
  5. Consider downstream system and workflow constraints when designing models intended for real-world use.

Skills

Required

  • PhD in Computer Science, Electrical Engineering, Biomedical Engineering, or a related field
  • 5–8+ years of post-PhD experience in medical imaging AI or applied AI research
  • Strong publication and/or patent record
  • Demonstrated ability to take work from research conception to validated, real-world deployment
  • Deep expertise in medical image analysis (2D/3D/4D imaging across MRI, CT, PET, X-ray, Ultrasound)
  • Advanced proficiency in PyTorch and modern deep learning workflows
  • Strong experience with multimodal models, self-/weakly-supervised learning, and cross-site generalization
  • Solid understanding of MLOps, experiment reproducibility, and AI development in regulated environments

Nice to have

  • Exposure to AI systems operating under real-world constraints, such as interaction with imaging hardware, devices, or operational pipelines
  • Familiarity with issues like sensor variability, latency, stability, or deployment constraints
  • Experience adjacent to robotics, intelligent devices, or simulation environments is a plus
  • Recognized for technical depth and scientific judgment in medical imaging AI
  • Thinks beyond individual models to end-to-end research and validation pipelines
  • Pragmatic, impact-driven, and comfortable operating across research, engineering, and clinical domains
  • Viewed as a trusted senior advisor by peers and leadership

What the JD emphasized

  • PhD-trained AI scientist with 5–8+ years of post-PhD experience
  • driving advanced medical imaging and multimodal AI research from conception to real-world clinical deployment
  • deep expertise in medical imaging
  • foundation models
  • publication record required
  • take work from research conception to validated, real-world deployment
  • AI development in regulated environments

Other signals

  • medical imaging
  • foundation models
  • multimodal AI
  • clinical deployment
  • research vision
  • state-of-the-art detection, segmentation, classification, and quantitative imaging solutions
  • transformers
  • large vision and multimodal models
  • self-supervised and weakly supervised learning
  • robustness, generalization, uncertainty handling, and evaluation
  • system and workflow constraints
  • clinically meaningful AI solutions
  • dataset curation and evaluation strategies
  • acquisition variability, site differences, and data quality challenges
  • clinical validation, regulatory awareness, and deployment
  • safety and reliability
  • AI platform, tooling, and modeling strategy
  • subject-matter expert in medical imaging AI
  • technical trade-offs and opportunities
  • publication in top-tier venues
  • foundation models, multimodal learning, and applied AI for healthcare
  • PyTorch
  • MLOps, experiment reproducibility, and AI development in regulated environments
  • AI systems operating under real-world constraints
  • sensor variability, latency, stability, or deployment constraints
  • end-to-end research and validation pipelines