Intern - AI / Data Science Engineering

Project Title:
Development of Data-Driven Insights and Predictive Models for Semiconductor Manufacturing

Project Description:
This project focuses on applying data analysis, statistical modelling, and machine learning techniques to large semiconductor manufacturing datasets.

The intern will explore how data-driven methods are used to identify patterns, detect anomalies, and develop predictive insights that enhance understanding of process behaviour. The project emphasises structured learning through advanced analytics, data engineering, and visualisation within a high-volume manufacturing environment.

Objective of the project:
To develop analytical models and data-driven insights that improve understanding of manufacturing processes and support informed decision-making.

Project Scope:

• Perform exploratory data analysis on manufacturing datasets to identify patterns, correlations, and trends
• Apply statistical methods such as hypothesis testing and regression analysis to evaluate data
• Develop and evaluate machine learning models for predictive analysis and pattern recognition
• Explore anomaly detection methods to identify deviations in data
• Design basic data workflows for data extraction, preparation, and feature development
• Create data visualisations or dashboards to present analytical insights
• Document approaches, assumptions, and findings in a structured manner

Learning Opportunities:

• Gain hands-on exposure to large-scale semiconductor manufacturing datasets
• Apply machine learning and statistical techniques to real-world engineering problems
• Develop skills in translating analytical results into meaningful insights
• Build understanding of data engineering concepts such as data preparation and feature development
• Strengthen communication skills through presentation of technical findings
• Gain exposure to data-driven decision-making in manufacturing environments

Deliverable:

• End-to-end analytical project including data exploration, modelling, and evaluation
• Machine learning models with documented performance and insights
• Data workflows for preprocessing and feature development
• Data visualisations or analytical reports summarising key findings
• Final presentation and project documentation

Impact of Project:

• Improved understanding of manufacturing data patterns and behaviours
• Contribution to data-driven approaches in process analysis and decision-making
• Development of scalable concepts for analytical modelling and insight generation

Skillsets Required:

• Programming skills in Python for data analysis and modelling
• Basic knowledge of statistics and machine learning concepts
• Familiarity with data querying tools (e.g. SQL)
• Strong analytical thinking and problem-solving skills
• Ability to communicate technical concepts clearly

Course of Interests:
The ideal candidate should be currently pursuing a degree in Electrical and Electronics Engineering, Materials Science, Physics, Data Science, Computer Science, Statistics, Mathematics, Artificial Intelligence, or a related field.

Duration Period:
Minimum 5 months