Data Scientist
We are seeking a Data Scientist to support our team for a 12-month contract - working as part of our growing data science chapter. This role involves working with a highly dynamic squad focusing on building digital products for our end users, enabling them to be more efficient, effective and deliver a great customer experience.
This position offers the opportunity to cultivate new skills within an agile work environment, working closely with a strong group of established data scientists, bolster business acumen, expand professional network, and, of course, a fun journey! The squads' diverse use cases extend from predictive maintenance, rail optimization, digital manufacturing, knowledge management with GenAl, and new product and catalyst development, providing a broad spectrum for skill development and innovation.
Are you eager to learn and grow, to broaden your horizons? Do you have passion for cutting-edge technologies and ways of working? If yes, then this opportunity to support our Digital Factory team could be a great fit.
Responsibilities:
Required Skills and Qualifications:
The Following Would Be Considered An Asset:
Additional Information:
This position offers the opportunity to cultivate new skills within an agile work environment, working closely with a strong group of established data scientists, bolster business acumen, expand professional network, and, of course, a fun journey! The squads' diverse use cases extend from predictive maintenance, rail optimization, digital manufacturing, knowledge management with GenAl, and new product and catalyst development, providing a broad spectrum for skill development and innovation.
Are you eager to learn and grow, to broaden your horizons? Do you have passion for cutting-edge technologies and ways of working? If yes, then this opportunity to support our Digital Factory team could be a great fit.
Responsibilities:
- Support initiatives to deliver safe, reliable, and competitive operations of our manufacturing assets
- Work closely with process, mechanical and automation engineers to solve complex problems by applying data science methodologies like machine learning, statistical algorithms and optimization
- Design and build explorative, predictive or prescriptive models, utilizing optimization, simulation and machine learning techniques
- Collaborate with data architects and data developers to build scalable data pipelines and deploy models from development to production environment on edge or in the cloud
- Ensure timely analysis and testing for regular maintenance of solutions over time
- Translate business requirements into technical prototypes and solutions
- Communicate the design, functionality and output of the analytical models/solutions developed
Required Skills and Qualifications:
- Bachelor's or Graduate Degree in engineering, computer science, mathematics, physics, or a similar technical discipline
- 0-5 years of experience in statistics and machine learning, which may include work with time series, images and text data processing
- Proficiency in Python and related data science libraries (e.g., pandas, scikit-learn, etc.)
- Experience executing projects, both working independently and as part of a cross-functional team
- Excellent written and verbal communication skills
- Openness to learning
- A safety-minded individual actively contributing to a safe working environment
The Following Would Be Considered An Asset:
- Engineering knowledge especially in the process and petrochemical industry
- Experience in deploying models in a production environment (particularly using cloud platforms such as Azure Databricks)
- Experience with building LLM applications (e.g., few-shot prompting, RAG, and vector search for tasks like text generation and retrieval)
Additional Information:
- This role may require infrequent ad hoc travel to operating sites in Joffre, Sarnia, or Geismar
- Candidate must be eligible to travel easily between Canada and the United States
- This position provides the option to work under a hybrid model, from home on Mondays and Fridays each week, and at the office Tuesday, Wednesday and Thursday