Description
The data scientist supports the AI innovation unit in the analysis, modeling, and use of departmental data to develop relevant, ethical, and effective AI solutions. They are responsible for supporting the unit in the design, evolution, and integration of AI solutions within departmental systems, ensuring their alignment with the principles of digital sovereignty, security, and data governance.
Requirements
Eligibility criteria (minimum requirements): Must speak, write and understand French.
1. Possess an undergraduate university degree (Bachelor's) in computer science, business administration, or a related field
2. Have obtained training in artificial intelligence or a related field, attested by a university degree or a recognized professional certification in data science (e.g., Microsoft Certified Data Scientist, IBM Data Science Professional Certificate)
3. Possess six (6) years of professional experience in information technology, including three (3) years as a data scientist, including the production of data analysis, modeling, or visualization deliverables.
4. Have participated in two (2) projects of more than 500 person-days each as a data scientist, including the implementation of data pipelines, predictive or analytical modeling
5. Possess two (2) years of experience in information or data architecture/modeling
6. Possess three (3) years of experience with the SQL language
Bonuses
Benefits
Horaire de travail : 35 heures/semaine
Mode de travail : 100% Télétravail mais doit résider à Québec
Salaire annuel: 52 000-70 000 $
Responsibilities
Activities
• Identify and prepare the datasets needed for AI use cases
• Design and train machine learning and generative AI models
• Evaluate the performance of the models and propose improvements
• Collaborate with business teams to translate needs into algorithmic solutions
• Document the work and contribute to the governance of data and algorithms
• Participate in the creation of a repository of reusable AI models
• Establish performance and benefit indicators for AI solutions
• Contribute to AI monitoring and the evolution of internal data science practices
Deliverables
• Prepared and documented datasets for AI use cases
• Trained and evaluated machine learning models
• Data pipelines (setup and documentation)
• Validated predictive and analytical models
• Documentation of work and models (governance)
• Repository of reusable AI models
• Performance indicators for AI solutions
• Monthly timesheets
• Monthly progress reports