Staff Data Scientist

Staff Data Scientist (LPB1) – Enterprise AI

Role Overview

GE HealthCare is accelerating its transformation through a series of strategic “Big Bets” in Commercial excellence, Logistics optimization, Inventory management, and Manufacturing innovation. The Enterprise AI team, part of the Chief Data and Analytics Office, is at the forefront of delivering robust, enterprise-grade AI and ML solutions that drive measurable business impact at scale.
As a Staff Data Scientist (LPB1), you will play a key role in shaping and executing our AI strategy. You’ll collaborate across a unified, cross-functional delivery organization—partnering with experts in data engineering, ML engineering, analytics, GenAI development, and Corporate Finance—to solve complex business challenges and deliver scalable solutions.

Core Responsibilities

• Model Development & Delivery: Design, build, and deploy AI/ML models that address business challenges in commercial operations, supply chain, manufacturing, and related domains.
• Project Execution: Lead and contribute to data science projects from ideation through implementation, ensuring solutions are robust, scalable, and aligned with organizational priorities.
• Cross-Functional Collaboration: Work closely with colleagues in data engineering, ML engineering, analytics, GenAI, and business stakeholders to deliver integrated solutions and drive project success.
• Technical Excellence: Apply best practices in data preparation, feature engineering, model selection, validation, and deployment. Participate in code reviews and contribute to technical documentation.
• Continuous Improvement: Stay current with advancements in ML, GenAI, and analytics. Proactively identify opportunities to improve existing models and processes.
• Business Impact: Translate complex data science concepts into actionable insights for business partners, supporting decision-making and measurable outcomes.
• Mentorship & Learning: Share knowledge with peers, contribute to team learning, and seek opportunities for professional growth.

Experience Requirements

• Experience: 3+ years of direct AI/ML experience with demonstrated success building and deploying enterprise-grade solutions at scale.
• Relevant Expertise: Proven ability to apply advanced analytics and machine learning to solve real-world problems in areas such as forecasting, pricing, customer analytics, sales planning, computer vision, and operational optimization.
• Education: Bachelor’s, Master’s, or PhD in Computer Science, AI, Data Science, Applied Mathematics, or related field.
• Technical Skills: Proficiency in ML algorithms, GenAI frameworks, and data engineering. Experience with Python, AWS, Azure, and open-source tools (R, SQL, Spark, TensorFlow, Keras, PyTorch, Scikit-learn).
• MLOps & Engineering: Demonstrated ability to deploy, monitor, and maintain ML and GenAI models in production environments.
• Industry Knowledge: Understanding of analytics practices relevant to commercial operations, logistics, inventory management, manufacturing, and finance.
• Communication: Strong ability to convey complex technical concepts to non-technical stakeholders and senior executives.
• Interpersonal Skills: Proven success working in cross-functional, hybrid, and virtual teams.
• Ethics & Integrity: Commitment to ethical data science practices.
• Travel: Willingness to travel for business meetings as needed.

Additional Information