GE HealthCare
AI

Sr Data Scientist

GE HealthCare · Kraków, ML, PL · $234k

Actively hiring Posted about 10 hours ago

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.

Tags & focus areas

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