Data Scientist

Introduction

Brunel is currently seeking a Full Stack Data Scientist for a leading Chemical Manufacturing client in Calgary, Alberta. This is an exciting opportunity to join a forward-thinking Digital Factory team that is driving digitalization efforts across key value streams, including Integrated Value Chain, Reliability, Maintenance & Turnaround, and Quality & Commercial Excellence.

In this role, you will develop and deploy advanced analytics solutions that optimize decision-making and enhance operational performance. From predictive maintenance and rail optimization to digital manufacturing and GenAI-driven knowledge management, you will work on a variety of impactful initiatives that shape the future of the industry.

If you thrive in an agile, fast-paced environment and have a strong foundation in Python, machine learning, and cloud-based analytics, this is your chance to contribute to a cutting-edge digital transformation.

Responsibilities

Solution Development

• Translate business requirements into technical hypotheses and execute proof-of-concept (PoC) initiatives
• Develop and refine advanced analytics models, including forecasting, optimization, and GenAI

• Deploy scalable, production-ready solutions on Databricks
• Apply software engineering best practices, including version control and robust documentation

• Monitor and maintain production data science solutions, ensuring performance and reliability
• Address issues such as model drift and data pipeline failures
• Act as the primary interface between IT and the digital factory team to address sustainment needs

• Collaborate with technical and product teams to define requirements and deploy solutions
• Present clear, actionable insights for technical and non-technical audiences
• Translate complex data science outputs into business recommendations

Requirements

• Bachelor's, MSc, or PhD in engineering, computer science, or related fields (chemical or mechanical engineering preferred)
• Proficiency in Python and related data science libraries
• Strong foundation in data science, including machine learning, large language models, optimization, and statistics
• 2-6 years of practical experience in data science solution development
• Experience in the full data science lifecycle from PoC to deployment
• Familiarity with cloud platforms and tools for scalable deployments (Databricks, MLflow, Apache Spark)
• Expertise in monitoring and maintaining production systems, resolving model drift, and improving performance monitoring
• Proficiency in version control tools and best practices (Git, branching, merging)
• Strong problem-solving skills and adaptability in tackling ambiguous challenges
• Effective communication skills to present findings to diverse stakeholders
• Experience working in agile environments and managing workloads within sprints