GE Aerospace

Senior Data Scientist

GE Aerospace
Aircraft MRO & Aviation EngineeringSingapore, Central Singapore, SingaporeOnsitePosted 2 months ago

About the role

AI summarised

Join GE Aerospace in Singapore to lead the end-to-end development of computer vision and multimodal models for advanced defect detection in aerospace components. This role sits at the intersection of AI, imaging, robotics, and NDT, translating complex shop-floor inspection workflows into robust, scalable digital solutions.

AerospaceOnsiteDigital Technology / IT

Key Responsibilities

  • Lead end-to-end development of computer vision and multimodal models for defect detection, segmentation, classification, and anomaly detection.
  • Design experiments, select appropriate algorithms, define success metrics, and drive model iteration for inspection solutions.
  • Define data and imaging requirements for cameras, lighting, laser/optical sensors, and NDT equipment.
  • Co-design AI-ready, repeatable inspection cells and workflows considering hardware constraints and shop-floor conditions.
  • Partner with data engineers on ETL pipelines and data architecture (e.g., Databricks, AWS S3).
  • Contribute to scalable model deployment and monitoring in production environments (on-prem, cloud, and edge devices).
  • Translate shop-floor workflows and inspection standards into data science problems and product features by collaborating with domain experts.

Requirements

  • Master’s or PhD in Computer Science, Electrical/Computer Engineering, AI, Applied Mathematics, Statistics, or a related quantitative field.
  • 5+ years in data science/ML, including 3+ years specifically in computer vision or industrial inspection.
  • Strong foundational knowledge in Machine Learning/Deep Learning.
  • Proven experience with CNNs, transformers, segmentation, object detection, and anomaly detection.
  • Ability to combine hands-on computer vision/deep learning with a practical mindset for industrial deployment.
  • Strong collaboration, problem-solving, and influencing skills in an ambiguous environment.