Job Description Summary

The Additive Analytics team owns and develops software analytics products for Colibrium Additive machines at GE Aerospace. We transform machine, process, image, and quality data into models and insights that improve safety, quality, delivery, and cost for our customers.
As a Staff Data Scientist, you will lead the development and deployment of advanced analytics and machine learning solutions, including image-based methods, to monitor and optimize additive manufacturing processes and machine performance. You will shape the technical roadmap, partner closely with engineering and product teams, and help operationalize models into robust, production-grade software using modern software engineering practices.

Job Description

Site Overview

Established in 2000, the John F. Welch Technology Center (JFWTC) in Bengaluru is our multidisciplinary research and engineering center. Engineers and scientists at JFWTC have contributed to hundreds of aviation patents, pioneering breakthroughs in engine technologies, advanced materials, and additive manufacturing.

Roles and Responsibilities

• Technical Leadership in Data Science
  
  • Lead the design, development, and deployment of machine learning models for additive manufacturing use cases (e.g., anomaly detection, predictive quality, remaining useful life, process optimization).
  • Serve as a technical expert for data science within the Additive Analytics team, influencing methodology, standards, and solution strategy.
  • Mentor and coach other data scientists and engineers on ML best practices, experimentation design, and model evaluation.
• Image Analytics for Additive Manufacturing
  
  
  • Develop and apply computer vision / image analytics techniques to data such as layer images, in-situ monitoring images, or post-build inspection results.
  • Evaluate and implement methods including classical image processing and deep learning-based vision models (e.g., CNNs) for defect detection, feature extraction, and part quality assessment.
  • Partner with hardware and software teams to ensure image data is captured, stored, labeled, and curated effectively for analytics.
• Model Development, Validation & Deployment
  
  
  • Own the end-to-end ML lifecycle: problem framing, data exploration, feature engineering, model selection, training, validation, and performance monitoring.
  • Work with software engineers to operationalize models within production systems (e.g., APIs, batch pipelines, edge/on-machine deployment).
  • Define and track model performance metrics, monitor for drift and degradation, and design retraining strategies to ensure sustained value and reliability.
• Software Engineering Best Practices
  
  
  • Apply software development best practices to data science work, including modular design, clean code, and clear documentation.
  • Use Git-based workflows (branches, pull requests) and participate in peer code reviews to ensure code quality, maintainability, and knowledge sharing.
  • Collaborate with software and platform engineers to integrate models and pipelines into CI/CD workflows, enabling automated testing, packaging, and deployment of ML components.
  • Write and maintain unit, integration, and regression tests for data processing and modeling code to ensure reliability, reproducibility, and safe changes.
  • Follow established SDLC and coding standards within the Additive Analytics team and contribute to continuous improvement of these standards and tools.
• Data, Analytics Platform & Collaboration
  
  
  • Collaborate with data engineers and platform teams to shape data schemas, pipelines, and data quality standards for sensor, image, and operational data.
  • Work with system engineers, product managers, and domain experts to understand customer workflows, CTQs, and service requirements, and translate them into analytics solutions.
  • Contribute to the design of dashboards and analytics workflows that surface model outputs clearly to operators, engineers, and leaders.
• Business Impact & Problem Framing
  • Frame complex, ambiguous problems into tractable data science projects with clear hypotheses, success criteria, and business impact.
  • Quantify the value and tradeoffs of analytics solutions (e.g., accuracy vs. complexity; latency vs. model sophistication) and communicate these to stakeholders.
  • Support customer-facing teams by explaining model behavior, limitations, and insights in a clear, non-technical way when needed.
• Quality, Compliance & Continuous Improvement
  
  
  • Ensure analytics solutions adhere to GE Aerospace standards for safety, quality, security, and compliance.
  • Promote reproducible science through robust experimentation tracking, environment management, and documentation.
• Continuously scan for new methods and tools in ML, computer vision, and industrial analytics, and identify opportunities to apply them to additive problems

Ideal Candidate

Should be handson in AI/ML algorithms, awareness of image analytics, computer vision, experience in industrial problem solving.

Required Qualifications

• This role requires basic experience in the Engineering/Technology & Service Engineering. Knowledge level is comparable to a Bachelor's degree from an accredited university or college ( or a high school diploma with relevant experience)

Desired Characteristics

• Strong oral and written communication skills.
• Strong interpersonal and leadership skills.
• Ability to influence others and lead small teams. Lead initiatives of moderate scope and impact.
• Effective problem identification and solution skills.

At GE Aerospace, we have a relentless dedication to the future of safe and more sustainable flight and believe in our talented people to make it happen. Here, you will have the opportunity to work on really cool things with really smart and collaborative people. Together, we will mobilize a new era of growth in aerospace and defense. Where others stop, we accelerate

Additional Information

Relocation Assistance Provided: Yes