Senior Staff Data Scientist, Enterprise AI

Job Description SummaryAs a Senior Staff Data Scientist (Enterprise AI), you will work in and lead teams as technical domain expert 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 a Senior Staff Data Scientist (Enterprise AI), 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 deep forecasting experience to join our dynamic team.

Responsibilities

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

• Collaboration: Partner with leaders in various departments to identify business needs and deliver fit-for-purpose forecasts that drive tangible business value

• Technical Implementation: Establish forecasting standards, tools, and practices, ensuring best practices in model development, including cross-validation and hyperparameter tuning.

• MLOps / Engineering: Work with MLOps to streamline model deployment, monitoring, and maintenance. Implement CI/CD practices for robust forecasting solutions and optimize machine learning models.

• Business Outcomes: Connect forecasting efforts to tangible business outcomes by aligning forecasts with strategic business objectives. Use forecasting to identify risks and opportunities, driving action-oriented decision-making and planning.

• System Integration: Ensure that forecasting models are integrated with other business systems to provide a holistic view of business performance. Collaborate with IT and other technical teams to ensure smooth data flow and system interoperability.

• Thought Leadership: Stay updated with advancements in forecasting and AI, identify new opportunities for data science solutions, and influence executive leaders in the strategic use of ML, AI, GenAI, and advanced analytics.

Requirements

• Experience: strong AI/ML experience, including forecasting experience.

• Education: Masters/PhD in Statistics, AI, Economics, Statistics, Applied Mathematics, Operations Research or a related field.

• Technical Expertise: In-depth knowledge of forecasting methodologies, including, time-series forecasting, probabilistic simulation, financial modeling, and stochastic optimization

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

• MLOps and ML Engineering: Experience working with MLOps practices and ML Engineering skills to deploy, monitor, and maintain machine learning models in production. Ability to collaborate effectively with MLOps COE.

• Industry Knowledge: Knowledge of business analytics and practices relevant to the healthcare/MedTech/pharmaceutical/biotech industries. Ability to continuously track, evaluate, adapt the latest advancements in deep learning techniques and AI/ML research to business use cases across GE HealthCare.

• Communication: Ability to communicate complex forecasting and technical ideas to non-technical stakeholders, including senior executives.

• Interpersonal Skills: Proven ability to work effectively in cross-functional, often virtual and matrix teams.

• Ethics and Integrity: Impeccable ethics and integrity.

• Travel: Willingness to travel for regular internal and external business meetings.

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