Data Scientist

Trans Mountain Corporation operates Canada’s only pipeline system transporting oil products to the West Coast. We deliver approximately 890,000 barrels of petroleum products each day through a dual pipeline system of more than 1,150 kilometres of pipeline in Alberta, British Columbia and Washington state.

Trans Mountain also operates a state‑of‑the‑art loading facility, Westridge Marine Terminal, with three berths providing tidewater access to global markets.

As a federal Crown corporation, Trans Mountain continues to build on more than 70 years of experience delivering operational and safety excellence through our crude oil pipeline system.

With our expanded pipeline system now in place, Trans Mountain provides enhanced direct access for Canadian crude oil to world markets. The expansion realizes a world‑class system for oil transport, developed to Canada’s high standards within one of the most stringent regulatory regimes in the world, creating long‑term economic benefits, enhanced marine protection, enhanced safety and emergency management capabilities, and enhanced skilled‑worker capacity building in communities and Indigenous groups.

Our Core Values — Safety, Integrity, Respect and Excellence — guide our every step. Each obstacle we’ve overcome or success we’ve experienced has been the result of a shared commitment to living these values every day. Together, we’re focused on doing the right thing for each other and our communities. If you are looking for an opportunity to apply your strengths in an environment that encourages Safety, Integrity, Respect, and Excellence, we invite you to explore this opportunity with us!

Location: Calgary, AB

Department: IT Enablement & Innovation

Reports to: Manager, Data & Innovation

Worker Type: Employee

Schedule Type: Full‑time

Work Setting: In‑office

Trans Mountain is seeking a collaborative, motivated, and values driven individual to join our dynamic team as a Data Scientist. In this position, you will play a key role in contributing to the design, development, and deployment of AI‑driven solutions that support business objectives. This role requires hands‑on experience with machine learning techniques, data processing, and modern development practices.

The incumbent is a strong, independent problem‑solver who collaborates with senior engineers and cross‑functional teams.

• Develop, train, and validate machine learning and deep learning models under the guidance of senior team members.
• Conduct experiments to improve model accuracy, efficiency, and reliability.
• Apply best practices for versioning, testing, and documenting model development.
• Integrate AI components into existing systems or new applications.
• Translate technical requirements into practical, user‑focused solutions.
• Collect, clean, and preprocess datasets to support model development.
• Perform exploratory data analysis to identify trends, patterns, and potential issues.
• Stay informed about emerging AI tools, frameworks, and methodologies.
• Evaluate new techniques and propose improvements to existing workflows.
• Participate in internal knowledge‑sharing sessions and contribute to team learning.
• Assist in setting up monitoring tools to track model performance and data quality.
• Investigate issues such as drift or unexpected outputs and recommend corrective actions.
• Help retrain or update models to maintain long‑term effectiveness.
• Work with product managers, analysts, data and software engineers and stakeholders to understand requirements and identify AI opportunities.
• Communicate findings and technical concepts clearly to both technical and non‑technical audiences.
• Participate in planning and estimation activities for AI‑related projects.
• Maintain clear documentation for models, data processes, and system components.
• Prepare summaries of experiments, performance metrics, and recommendations.
• Ensure documentation is organized and accessible for team use.
• Apply ethical AI principles, including fairness, transparency, and privacy considerations.
• Assist in identifying potential biases or risks in datasets and models.
• Follow organizational guidelines and regulatory requirements related to AI development.