Job Description

Summary

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As an Additive Analytics Intern, you'll explore and prototype methods to forecast machine health for additive manufacturing (3D printing). You'll help identify early indicators of degradation or drift, analyze sensor and event logs, and build simple models and visuals that flag risk to print quality, uptime, and maintenance needs. This role is designed for early undergraduates; a strong curiosity and willingness to learn are most important

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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

Bachelor's 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

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Relocation Assistance Provided:

No