Role overview
GE HealthCare is advancing the future of medical technology through intelligent systems powered by AI.
As a Sr Data Scientist within our Global Services – Service Technology team, you will lead the development of cutting-edge machine learning and generative AI solutions that enhance imaging system performance, enable predictive maintenance, and improve patient outcomes.
This role offers the opportunity to work on high-impact projects in a collaborative, agile environment, driving innovation across healthcare operations and customer-facing products.
What you'll work on
- Leading the design, development, and deployment of AI/ML models for remote diagnostics, predictive maintenance, and operational optimization.
- Analyzing large-scale machine and service datasets to uncover actionable insights and inform product improvements.
- Collaborating with cross-functional teams including engineering, product management, and MLOps to integrate AI solutions into commercial applications.
- Appling statistical, machine learning, and optimization techniques to solve complex healthcare challenges.
- Developing and operationalizing GenAI solutions, including RAG architectures and AI agents using AWS, Azure, and open-source tools.
- Ensuring scalability, reusability, and high-quality standards across AI products and pipelines.
- Communicating technical findings and strategic recommendations to stakeholders across business and technical domains.
- Mentoring junior team members and promote a culture of data-driven decision-making and continuous learning.
What we're looking for
- M.S. or Ph.D. in Computer Science, Data Science, Engineering, or a related STEM field.
- Advanced experience in AI/ML development, with a strong portfolio of deployed models.
*Desired Characteristics:
Technical Expertise**
- Experience in diagnostics/prognostics, system health monitoring, and reliability engineering.
- Strong foundation in applied analytics, statistical modeling, and feature engineering.
- Skilled in data cleaning, data quality assessment, and exploratory data analysis.
- Proficiency in Python and data science tools (e.g., Jupyter, Scikit-learn, TensorFlow, PyTorch).
- Experience with cloud platforms (e.g., AWS) and big data technologies (e.g., Spark).
- Hands-on experience with deep learning architectures (CNNs, RNNs, GANs).
- Familiarity with GenAI tools (e.g., AWS Bedrock) and RAG models.
- Knowledge of cloud-native AI development and deployment practices.
- Experience in healthcare or industrial AI applications.