Senior Data Scientist, Enterprise AI
GE HealthCare
This listing was originally posted on GE HealthCare's careers page. Formulate is an equal opportunity job aggregator and is not involved in the hiring process. Where salary information is estimated, it is derived from BLS industry benchmarks and may differ from actual compensation.
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Core Responsibilities
Developing and implementing advanced AI algorithms and models to solve complex problems across finance, manufacturing, quality, supply chain, sales & marketing.
Developing and implementing advanced forecasting methodologies using technologies like batch forecasting, deep learning, simulation, and reinforcement learning to enhance decision-making.
Appling computer vision methods to drive manufacturing efficiencies and improve product quality across the GE Healthcare manufacturing network.
Collaborating with cross-functional teams to integrate AI solutions into various applications and products.
Analyzing large datasets to extract meaningful insights and improve model performance.
Designing and conducting digital experiments to validate and optimize AI models.
Presenting findings and recommendations to stakeholders in a clear and concise manner.
Collaborating with the MLOps team during AI model development, deployment and monitoring.
Proactively identifying new opportunities to further leverage data science solutions – prioritizing opportunities with the biggest potential benefit to the business.
Ensuring best practice adoption within the Enterprise AI team, applying appropriate levels of technical capability, standardization, and subject matter expertise.
Driving a culture of analytics and fact-based decision making through the utilization of standard methodologies and approaches.
Experience Requirements
M.S. or Ph.D. in Computer Science, Data Science, Engineering or a STEM related field with 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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Placement within this range depends on:
Relevant skills and qualifications
Prior job-related experience
Internal equity considerations (alignment with colleagues in similar roles) e.t.c.
We review pay ranges regularly to ensure they remain competitive with the external market and align with our internal equity considerations.
In addition to base salary, our employees have access to a comprehensive package of benefits and allowances, which may include:
Health & wellness coverage
Retirement and or savings plans
Allowances or benefits to support role requirements (e.g., mobility, transport, or role-specific needs such as a company car or allowance where applicable)
Work-life balance support (e.g., flexible working, leave programs)
Recognition and incentive programs aligned with performance and company success
The exact benefits package depends on the role, location, and employment terms as specified in the Colleague Value Proposition document that will be shared prior to the interview or at the offer discussion stage.
Performance Bonus: Details to be shared during offer discussions
Relocation Assistance Provided: No
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