First Role:

Job Description:

• Conduct time studies on existing preventive maintenance tasks to understand the current process and identify inefficiencies.
• Analyze data to identify patterns, bottlenecks, and opportunities for improvement in maintenance workflows.
• Propose and design process improvements to make preventive maintenance tasks faster and more efficient.
• Collaborate with the maintenance team to test and implement proposed process improvements.
• Create and maintain detailed documentation of the time study results, process changes, and improvement recommendations.
• Present findings and progress updates to department managers and senior leadership.

‍

‍

Second Role:

Job Description:

• Utilize machine learning models to analyze large datasets and identify patterns related to system performance and reliability.
• Design, develop, and refine machine learning models to predict potential failures and recommend proactive interventions.
• Collaborate with the team to validate model accuracy and ensure its applicability in real-world scenarios.
• Assist in the development of a web-based system to track predictive maintenance insights and provide real-time updates.
• Document findings and present actionable insights to stakeholders.

‍

‍

Qualifications

First Role:

• Currently pursuing a degree in Industrial Engineering, Mechanical Engineering, or a related field.
• Strong analytical and problem-solving skills.
• Knowledge or coursework in time study, process optimization, and Lean principles (e.g., Kaizen, Six Sigma) is a plus.
• Proficiency in Microsoft Excel; experience with data analysis and process mapping tools is a plus.
• Excellent communication skills, both written and verbal.
• Self-motivated, able to work independently and within a team environment.
• Strong attention to detail and ability to work in a fast-paced environment.
• Fluent in spoken & written English
• Preferably able to commit fully for 6 months (full-time internship).

‍

Second Role:

• Experience or coursework in developing and implementing machine learning models (e.g., using Python, TensorFlow, or Scikit-learn).
• Proficiency in JavaScript, along with basic knowledge of HTML and CSS, is required.
• Knowledge of Tableau is an added advantage but not compulsory.
• Strong analytical and problem-solving skills, with a passion for working with big data.
• Basic understanding of predictive maintenance concepts, failure prediction strategies, and web-based system development.
• Currently pursuing a degree in Data Science, Computer Science, Engineering, or a related field.
• Fluent in spoken & written English
• Preferably able to commit fully for 6 months (full-time internship).

‍

‍

Additional Information

• Internship positions available for intake starting January 2025 and onward.
• Please include internship start date and end date in the application.
• Open to Local Students who are required to complete an internship as part of their academic program.

‍