Data scientist Jobs in Toronto, ON

Assignment: RQ00242 - Data Science Developer - Senior

Requisition: RQ00242

Job Title: Senior Data Analytical Specialist/Scientist

Client: Metrolinx

Start Date: 2026-11-23

End Date: 2027-05-23

Department: Enterprise AI & Data Centre of Excellence

Office Location: 277 Front St W, Toronto

Business Days: 130.00

Location: Hybrid 3 days a week onsite

Public Sector Experience: preferred but not required

Must Haves:

• Deep expertise in statistics, probability, predictive modelling and machine learning, including classification, regression, clustering, feature engineering, experimentation, model selection, validation, bias/variance, overfitting and statistical inference.
• Advanced hands-on proficiency in Python, SQL and ML frameworks, with demonstrated experience building, tuning and validating models using real-world datasets. Tech stack: Python, SQL, Power BI, VSCode, Git, Jira, Azure (OpenAI, Foundry and ML Studio), Databricks.
• Strong hands-on experience with Azure Machine Learning and Microsoft Foundry/Azure AI Foundry, including end-to-end ML pipelines covering experimentation, training, evaluation and operationalization.

Description

• The Senior Data Analytical Specialist/Scientist will develop advanced analytics, predictive models, and AI-driven insights to address priority business opportunities. The role will apply statistical, machine learning, and analytical techniques to enterprise data, while supporting data maturity assessments, model validation, business-value analysis, and the identification of high-value AI and analytics opportunities.

Key Responsibilities

• Design and develop advanced analytics, predictive models, and machine learning solutions for priority business use cases.
• Conduct exploratory data analysis to identify patterns, trends, relationships, and opportunities for improved decision-making.
• Translate business problems into appropriate analytical and modelling approaches, defining hypotheses, methodologies, and success measures.
• Assess the quality, availability, and readiness of data required for AI and advanced analytics use cases.
• Support enterprise data maturity and analytics capability assessments and identify opportunities for improvement.
• Validate model performance using appropriate statistical and machine learning techniques and communicate model assumptions, limitations, and results.
• Partner with business stakeholders to identify high-value AI and analytics opportunities and assess potential business value and feasibility.
• Develop prototypes and proofs of concept to test analytical approaches before broader implementation.
• Work closely with Data Engineers, Data Architects, Product Managers, and technology teams to operationalize successful models and analytical solutions.
• Define and monitor analytical KPIs and support evaluation of whether delivered solutions achieve intended business outcomes.
• Communicate complex analytical findings and recommendations in clear, actionable terms for both technical and executive audiences.
• Apply appropriate Responsible AI, privacy, data governance, and model risk considerations throughout the analytics lifecycle.

Qualifications & Experience

• Degree in Data Science, Statistics, Mathematics, Computer Science, Economics, Engineering, or a related quantitative field, or equivalent experience.
• Significant experience in data science, advanced analytics, predictive modelling, machine learning, or statistical analysis.
• Strong proficiency in Python, SQL, statistical analysis, and machine learning techniques.
• Experience developing and validating predictive and machine learning models using real-world enterprise data.
• Knowledge of model evaluation, experimentation, feature engineering, data quality, and analytical methodology.
• Experience working with modern cloud-based data and analytics environments.
• Understanding of Generative AI and emerging AI techniques would be an asset.
• Strong business acumen with the ability to connect analytical findings to measurable business outcomes.
• Strong communication, problem-solving, and stakeholder-management skills.