Job Description Summary

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.

Role Overview:

1. Data ingestion and quality:

• Perform basic data cleaning validation time alignment and documentation based on sensor data and event logs of the machine.

2. Feature exploration

• Engineer simple health indicators and explore correlations between indicators and outcomes like aborted builds rework and alarms

3. Forecasting prototypes

• Under guidance prototype lightweight forecasting/baseline methods (e.g. moving averages EWMA AR baseline simple classification thresholds)
• Compare methods using clear metrics (e.g. precision/recall for early warning lead time false-alarm rate)

4. Visualization and monitoring

• Build simple dashboards showing trailing indicators predicted risk bands and recent anomalies
• Create concise reports that explain findings to technical and non-technical audiences

5. Experiment design

• Help structure offline back tests and small A/B-style evaluations to assess alert usefulness
• Document assumptions data gaps and improvement ideas

6. Collaboration and knowledge capture

• Work with engineers and maintenance teams to understand failure modes and thresholds
• Standardize templates for data dictionaries feature lists and evaluation summaries

Ideal Candidate:

Should be pursuing the course.

Required Qualifications

• Bachelors student in Engineering Data Science Computer Science Applied Math or related field
• Comfortable with basic statistics and time series concepts (trends seasonality moving averages)
• Proficient with Excel or Google Sheets; exposure to a programming language (e.g. Python) from coursework or self-learning
• Strong communication organization and teamwork skills
• Interest in predictive maintenance reliability or analytics for manufacturing

Desired Qualifications (Nice to Have)

• Basic Python data stack exposure (pandas matplotlib/seaborn)
• Intro knowledge of anomaly detection or forecasting concepts (e.g. z-scores EWMA AR/ARIMA at a high level)
• Familiarity with additive manufacturing data types (sensor logs alarms maintenance records)
• Experience with simple dashboards
• Understand how forecasting and anomaly detection can improve uptime quality and maintenance planning
• Gain hands-on experience with time series preprocessing feature engineering and baseline models
• Learn to evaluate alert quality and communicate tradeoffs (lead time vs. false alarms)
• Build practical dashboards and reports for stakeholders
• Exposure to additive manufacturing process

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