Data Science Summer Student

Westerkirk•Calgary, AB

3d•Onsite

About The Position

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.

Requirements

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

Nice To Haves

Experience with time-based validation
Experience with imbalanced datasets
Experience with MLOps concepts
Experience with APIs
Experience with cloud deployment
Experience with dashboards
Experience with maintenance/supply chain analytics

Responsibilities

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