As the Staff Data Scientist, you will work in teams addressing statistical, machine learning and data understanding problems in a commercial technology and consultancy development environment. These teams typically include statisticians, computer scientists, software developers, engineers, product managers, and end users, working in concert with partners in GE business units.
You will be part of a data science or cross-disciplinary team on commercially facing development projects, typically involving large, complex data sets. 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. The ideal candidate will be responsible for developing and deploying machine learning models. This role requires a strong technical background, excellent problem-solving skills, and the ability to work collaboratively with data engineers, analysts, and other stakeholders.Job Description
Roles and Responsibilities:
Design, develop, and implement machine learning models and algorithms.
Conduct experiments to evaluate model performance and iterate on model improvements.
Collect, preprocess, and analyze large datasets to be used for training and testing machine learning models.
Ensure data quality and integrity throughout the data pipeline.
Work with unstructured data, including images, video, and text.
Deploy machine learning models into production environments.
Monitor and maintain deployed models to ensure they perform as expected.
Experience with state-of-the-art computer vision models.
Develop and maintain pipelines for Retrieval-Augmented Generation (RAG) and Large Language Models (LLM).
Ensure efficient data retrieval and augmentation processes to support LLM training and inference.
Collaborate with data scientists to optimize RAG and LLM pipelines for performance and accuracy.
Utilize semantic and ontology technologies to enhance data integration and retrieval.
Ensure data is semantically enriched to support advanced analytics and machine learning models.
Work closely with data architects, data engineers, and other stakeholders to understand business requirements and translate them into technical solutions.
Provide technical support and guidance on machine learning-related issues.
Optimize machine learning models for performance, scalability, and efficiency.
Implement techniques to improve model accuracy and reduce computational costs.
Stay up to date with the latest advancements in machine learning and artificial intelligence.
Explore and implement new machine learning techniques and tools to enhance the team's capabilities.
Maintain comprehensive documentation of machine learning models, algorithms, and processes.
Ensure knowledge transfer and continuity within the team.
Minimum Qualifications:
Bachelor’s degree from accredited university or college with minimum of 4 years of professional experience OR associate's degree with minimum of 7 years of professional experience OR High School Diploma with minimum of 9 years of professional experience
Proficiency in Python (mandatory).
Experience with machine learning frameworks such as TensorFlow, PyTorch, or Keras.
Understanding of computer vision techniques and tools (e.g., OpenCV, YOLO, Mask R-CNN).
Experience with handling unstructured data, including images, videos, and text.
Familiarity with cloud platforms (e.g., AWS, Azure, Google Cloud) and their machine learning services.
Experience with data preprocessing and augmentation tools.
Familiarity with data visualization tools (e.g., Matplotlib, Seaborn) is a plus.
Strong analytical and problem-solving skills.
Excellent communication and collaboration abilities.
Ability to work in a fast-paced, dynamic environment.
Note: Military experience is equivalent to professional experience
Eligibility Requirement:
Legal authorization to work in the U.S. is required. We will not sponsor individuals for employment visas, now or in the future, for this job.
Desired Characteristics:
- Experience with deep learning and neural networks.
- Knowledge of data governance and compliance standards.
- Certification in cloud platforms or machine learning.
- Demonstrated awareness of how to succeed in ambiguous circumstances.