C++, Machine learning techniques, Python Programming, PyTorch, Scikit-learn, TensorFlow
The Principal Data Scientist will guide, lead, and work in teams as technical domain expert addressing machine learning, statistics, and data understanding problems in a commercial technology and consultancy development environment. In this role, you will bring predictive and prescriptive capabilities together by developing and deploying state of the art machine learning and statistical methods for solving challenging business problems in energy transition area, e.g., supply and demand forecasting, distributed grid optimization, predictive maintenance, digital inspection, etc.
Job Description
Roles and Responsibilities
As a Principal Data Scientist, you will guide, lead, or otherwise contribute to data science or cross-disciplinary team on commercially facing development projects, typically involving large, complex data sets. These teams typically include statisticians, operations research experts, computer scientists, software developers, engineers, product managers, and end users, working in concert with partners in GE business units and GE customers. You will drive innovation and lead the development of algorithms and systems for various industrial domains including power grid, renewable energy, power generation, and manufacturing. Potential application areas include predictive maintenance, cyber security, visual intelligence, supply and demand forecasting, operations optimization, natural language processing, etc.
Technical Expertise:
Domain Knowledge:
Demonstrated and externally recognized expertise in Operations Research or Machine Learning and industry trends
Leadership:
Personal Attributes:
Required Qualifications
General Electric Company is an American multinational conglomerate incorporated in New York City and headquartered in Boston. As of 2018, the company operates through the following segments: aviation, healthcare, power, renewable energy, digital industry, additive manufacturing and venture capital and finance.
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