Job Description Summary

The Senior Staff Data Scientist (Enterprise AI) will work in teams addressing statistical, machine learning and data understanding problems in a commercial technology and consultancy development environment. In this role, you will contribute to the development and deployment of modern machine learning, operational research, semantic analysis, and statistical methods for finding structure in large data sets.

Job Description

Role Overview

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.

As the Senior Staff Data Scientist, you will be at the forefront of developing and delivering innovative algorithms that generate actionable business insights for key areas within GE HealthCare, including Finance, Commercial, Supply Chain, Quality, Operational Excellence and Lean, and Manufacturing. We are seeking a highly skilled and motivated Data Scientist with experience specializing in computer vision AI applications to join our dynamic team.

Core Responsibilities

• Develop and implement advanced computer vision algorithms and models to solve complex problems across manufacturing, quality and supply chain.

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

• Conduct research and stay updated with the latest advancements in computer vision and AI technologies.

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

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

• Design and conduct experiments to validate and optimize computer vision models.

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

• Collaborate with the MLOps team to establish standards for computer vision model development, deployment and monitoring.

• Proactively measure and communicate value generation from developed computer vision algorithms to business and IT leaders across GE HealthCare.

• 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.

• Operating as a thought leader and visionary, with the ability to influence executive leaders in the strategic use of ML, AI, GenAI, and advanced analytics in decision-making and value creation.

Experience Requirements

• PhD in Computer Science, Data Science, Engineering or a STEM related field with a focus on neural networks and computer vision.

• Hands-on experience in developing and deploying computer vision 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.

• Experience with image processing techniques and libraries like OpenCV.

• Familiarity with deep learning architectures including CNNs, RNNs, and GANs.

• Expertise in data preprocessing, augmentation, and annotation for computer vision tasks.

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

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

• Ability to continuously track, evaluate, adapt the latest advancements in deep learning techniques and AI/ML research to business use cases across GE HealthCare.

• 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.

Additional Information