De Havilland Aircraft of Canada Limited (DHC) is a storied name in the aerospace industry, recognized worldwide for its pioneering contributions to aviation and its unwavering commitment to quality, innovation, and reliability. Headquartered in Calgary, AB, DHC currently has approximately 2,400 employees across British Columbia, Alberta and Ontario, as well as in markets and distribution hubs world-wide.

Established in 1928, De Havilland Canada has a rich history marked by the development of some of the most iconic and versatile aircraft ever built. These aircraft have served a variety of roles—from bush flying to commercial aviation—and are celebrated for their rugged durability, operational versatility, and excellence in performance.

Over the decades, De Havilland Canada has evolved to meet the changing needs of the aviation industry. Today, we are more than just an aircraft manufacturer; we are a comprehensive aerospace company with capabilities that span design, production, maintenance, repair, and overhaul (MRO) services. Our operations are supported by a team of skilled engineers, technicians, and industry professionals who are dedicated to upholding the highest standards of craftsmanship and innovation.

Open to MRU Data Science Students

Project Overview:
De Havilland is seeking a senior capstone student or student team in Data Science, Computer Science, or a related discipline to develop a machine learning solution that predicts future parts requirements for scheduled maintenance events using historical maintenance and materials data.

Scheduled maintenance activities are planned in advance, but the exact parts required can vary based on aircraft usage, age, configuration, maintenance history, and prior inspection findings. More accurate prediction of future parts demand would improve maintenance readiness, reduce delays, lower excess inventory, and support better materials planning.

The objective of this project is to design, develop, validate, and demonstrate a production-ready system capable of forecasting likely part requirements for upcoming scheduled maintenance packages.

**Project Objectives:**The student will be expected to:

• analyze historical maintenance and parts consumption data
• identify key drivers of parts demand for scheduled maintenance events
• develop machine learning models to predict likely part requirements and quantities
• evaluate model performance against baseline planning methods
• design and implement a production-ready solution architecture suitable for operational deployment
• provide recommendations for integration into maintenance planning and inventory support processes

**Expected Deliverables:**The final project should include:

• a clearly defined problem statement and success criteria
• a cleaned, structured, and documented modeling dataset
• exploratory data analysis and feature engineering approach
• one or more predictive models for part demand forecasting
• validation results with business-relevant performance measures
• a production-ready system, including:
• repeatable data pipeline
• deployable model workflow
• documented system architecture
• version-controlled codebase
• error handling, monitoring, and retraining considerations
• final technical documentation and presentation to stakeholders

Qualifications

This project is well suited to a capstone course in Data Science, Machine Learning, Computer Science, Software Engineering, or Industrial Analytics, particularly for students interested in applied AI, predictive maintenance, and operations optimization.

Skills

• Python for data analysis and machine learning
• SQL and data wrangling across multiple operational datasets
• Machine Learning Model Development and evaluation
• feature engineering and handling of real-world data quality issues
• software engineering practices for production deployment
• documentation and communication of technical work to business stakeholders
• Experience with time-based validation, imbalanced datasets, MLOps concepts, APIs, cloud deployment, dashboards, or maintenance/supply chain analytics would be considered an asset.

Why work at De Havilland?

De Havilland Canada is a Canadian-owned and operated aircraft manufacturer dedicated to providing rewarding opportunities in a diverse and welcoming workplace.

• Work with a team that makes a true difference in the world-making it possible for people to travel around the globe efficiently, comfortably and safely.
• We’ve manufactured over 5,600 aircraft including the most advanced turboprop in the air today. Our experience and expertise in constructing the highest performing aircraft in the industry is second to none.
• Our state-of-the-art manufacturing facilities are dynamic workplaces led by teams that support and encourage all employees.
• With a passionate team of innovators and a global network of support, De Havilland Canada proudly carries on it’s tradition as a leader in aerospace.

In addition to plenty of opportunities for career growth in a stimulating work environment, De Havilland Canada offers:

• Competitive wages
• Paid vacation
• Extended health benefits (medical, dental, vision, paramedical)
• Life insurance
• RRSP/DPSP Plan
• Employee and family assistance program

At De Havilland Aircraft of Canada (DHC), we are committed to protecting our people, customers, shareholders and the public through Health & Safety Excellence. As such, it is expected that all employees maintain strict adherence to Health & Safety Policies and to perform key physical tasks of the position described in the job description and interview process. This may include but is not limited to the ability to work in a variety of environmental conditions including temperature extremes, confined spaces, working at heights and with or around chemicals. Employees are expected to adhere to the use of personal protective equipment (PPE) when at work which must include but is not limited to the ability to maintain a positive fit test when mask use is required.

At De Havilland Canada, we aim to be inclusive and diverse and provide equal opportunity for employment. All qualified applicants, regardless of gender, age, race, religion, sexual orientation, and disability, are encouraged to apply. De Havilland will accommodate the needs of applicants with disabilities throughout all stages of the selection process. If you need accommodation during the recruitment process, please advise your Talent Acquisition representative. Information relating to the need for accommodation and accommodation measures will be addressed confidentially.

Any offer of employment is conditional on the completion of positive and satisfactory background checks, which may include, proof that you are legally entitled to work in Canada, professional references, verification of employment history, verification of educational background and criminal background checks.