Sr Data Scientist

Summary The Sr Data Scientist will develop and implement Artificial Intelligence based solutions across various disciplines in GE Aerospace. In this role, the candidate will contribute to the development and deployment of machine learning and deep learning solutions focused on image and video analytics, including computer vision, image processing, multimodal models, statistical methods, and semantic analysis to extract structure and actionable insights from large-scale visual datasets. The candidate will be responsible for executing data science projects independently to deliver business outcomes and is expected to demonstrate domain expertise, develop, and execute program plans and proactively solicit feedback from stakeholders to identify improvement actions. This role requires a strong technical background, excellent problem-solving skills, and the ability to work collaboratively with stakeholders from different functional and business teams.

• Understand business problems and identify opportunities to implement Data Science and artificial intelligence solutions.
• Design, develop, and implement computer vision, image processing, and multimodal deep learning solutions across engineering, services, and manufacturing use cases, including vision tasks such as classification, detection, segmentation, tracking, OCR, and self-supervised representation learning
• Own the end-to-end pipelines for data acquisition, labeling standards, augmentation, dataset versioning, and governance for large scale image/video datasets
• Explore, evaluate, and apply state-of-the-art architectures (e.g., CNNs, Vision Transformers, hybrid CNNViT, diffusion-style, multimodal visionlanguage) tailored to aerospace imaging modalities.
• Understand various business processes pertaining to Analytics Development Process
• 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.
• Conduct experiments to develop model and evaluate model performance and iterate on model improvements.
• Work with Data Engineering teams to deploy data science models into production environments.
• Monitor and maintain deployed models to ensure they perform as expected.
• 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/deep 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, deep learning and artificial intelligence.
• Explore and implement new machine learning, deep learning and artificial intelligence techniques and tools to enhance the team's capabilities.
• Maintain comprehensive documentation of machine learning, deep learning models, algorithms, and processes.
• Ensure knowledge transfer and continuity within the team.

• The ideal candidate should have 5+Years experience into development and deployment of machine learning and deep learning solutions focused on image and video analytics, including computer vision, image processing, multimodal models, statistical methods, and semantic analysis to extract structure and actionable insights from large-scale visual datasets.

• Masters or PhD degree in Statistics, Machine Learning, Computer Science or related STEM fields (Science, Technology, Engineering and Math) with 5+Years analytics development experience
• Proficiency in Python (mandatory).
• Proficiency inPyTorch/TensorFlow, CNNs, Vision Transformers, hybrid CNNViT, diffusion-style, multimodal visionlanguage & OCRs.
• Strong analytical and problem-solving skills.
• Excellent communication and collaboration abilities.
• Ability to work in a fast-paced, dynamic environment.
• Familiarity with cloud platforms (e.g., AWS, Azure, Google Cloud) and their machine / deep learning & AI services.
• Familiarity with Python web frameworks.

• Influences PBs on their decisions.
• Implements a roadmap.
• Evaluates, down selects, and has awareness of advanced models for deriving new features in relation to the source of data.
• Can fit parameters for complex physics models.
• Develops ongoing hypothesis testing for model validity.
• Runs change point assessments and predictive models to project time series.
• Executes validation reproducibility, and deployment criteria.
• Able to use a variety of approaches (sequential sampling/ experimentation and observational studies) to generate cost-effective and representative samples.
• Understand advanced methods to trade off interpretability vs predictive complexity.
• Can articulate the top pitfalls and misleading graphical representations.
• Proficient with local and distributed operating environments.