Data Scientist Specialist, Enterprise AI

GE HealthCare’s Chief Data and Analytics Office team delivers innovative data, insights and AI solutions for the organization. Our Enterprise AI team works on a diverse portfolio of Machine Learning (ML), AI and GenAI projects, by combining agile and entrepreneurial drive with industry-leading methods and tools.

Responsibilities

• Develop and implement advanced AI algorithms and models to solve complex problems across finance, manufacturing, quality, supply chain, sales & marketing.

• Develop and implement advanced forecasting methodologies using technologies like batch forecasting, deep learning, simulation, and reinforcement learning to enhance decision-making.

• Apply computer vision methods to drive manufacturing efficiencies and improve product quality across the GE Healthcare manufacturing network.

• Collaborate with cross-functional teams to integrate AI solutions into various applications and products.

• Analyze large datasets to extract meaningful insights and improve model performance.

• Design and conduct digital experiments to validate and optimize AI models.

• Present findings and recommendations to stakeholders in a clear and concise manner.

• Collaborate with the MLOps team during AI model development, deployment and monitoring.

• Proactively identify new opportunities to further leverage data science solutions – prioritizing opportunities with the biggest potential benefit to the business.

• Ensure best practice adoption within the Enterprise AI team, applying appropriate levels of technical capability, standardization, and subject matter expertise.

• Drive a culture of analytics and fact-based decision making through the utilization of standard methodologies and approaches.

Experience Requirements

• B.S. in Computer Science, Data Science, Engineering or a STEM related field with basic hands-on experience in developing and deploying AI models and applications.

• Proficiency in the latest Python, AWS, Azure, and open-source data science tools such as Jupyter, R, SQL, Hadoop, Spark, TensorFlow, Keras, PyTorch, and Scikit-learn.

• Proficiency in AI agentic development tools such as AWS Bedrock, Azure AI Foundry, Microsoft Copliot Studio and LangChain/LangGraph.

• Knowledge of RAG architecture, including retrieval mechanisms and generative models.

• Knowledge of deep learning architectures including CNNs, RNNs, and GANs.

• Experience building RAG models and AI agents via AWS or Azure AI services.

• Ability to work with large-scale datasets and perform efficient data analysis.

• Expertise in transformers, self-supervised learning, and generative AI models.

• Strong problem-solving skills and the ability to think critically and creatively.

• Excellent communication skills and the ability to work collaboratively in a team environment.

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