Metrolinx is connecting communities across the Greater Golden Horseshoe. Metrolinx operates GO Transit and UP Express, as well as the PRESTO fare payment system. We are also building new and improved rapid transit, including GO Expansion, Light Rail Transit routes, and major expansions to Toronto’s subway system, to get people where they need to go, better, faster and easier. Metrolinx is an agency of the Government of Ontario.

At Metrolinx, equity, diversity and inclusion are essential to living our values of serving with passion, thinking forward and playing as a team.

Metrolinx's Innovation and Information Technology group supports female team members via "Go Tech Women" an affinity group for women in Information Technology, led by our Chief Information Officer.

If you enjoy technology and innovation, value diversity, appreciate work/balance and are looking for an opportunity to make a better world via public service, Metrolinx would like to hear from you

The Enterprise AI & Data Centre of Excellence (CoE) within I⁢ delivers trusted data, analytics, and AI solutions that help Metrolinx make better decisions and improve business outcomes. As a Data Scientist, you will partner with business stakeholders and technical teams to analyze data, develop machine learning models, build data pipelines, and generate insights that support strategic and operational decision-making. This role offers the opportunity to work with up-to-date data and AI technologies while contributing to enterprise-wide innovation and continuous improvement.

What will I be doing?

- Design, develop, and maintain scalable data pipelines and analytical solutions using Microsoft Azure, Databricks Lakehouse, Azure AI Foundry, Unity Catalog, Delta Lake,



and Azure Data Lake Storage to support enterprise analytics, artificial intelligence, and machine learning initiatives.
- Build, curate, and manage trusted datasets through data ingestion, transformation, integration, and quality validation processes using SQL, Python, PySpark, and Databricks.
- Perform exploratory data analysis (EDA) to identify trends, patterns, anomalies, and opportunities that support business and operational decision-making.
- Develop, validate, deploy, and monitor machine learning and AI models using regression, classification, clustering, forecasting, and generative AI techniques to solve business challenges.
- Leverage Azure AI Foundry and Azure AI services to develop, evaluate, and operationalize AI applications, including generative AI, intelligent agents, and retrieval-augmented solutions.
- Collaborate with business stakeholders to understand requirements, define success metrics, and translate business problems into scalable data science, analytics, and AI solutions.
- Partner with Data Engineers, AI Engineers, and Solution Architects to operationalize machine learning and AI solutions through MLOps and LLMOps best practices, including model deployment, monitoring, retraining, and lifecycle management.
- Extract, integrate, and analyze large-scale enterprise data from Azure cloud platforms, Databricks environments,



and operational systems using distributed computing frameworks.
- Develop reusable notebooks, feature engineering pipelines, semantic models, and AI assets that improve the scalability and consistency of enterprise analytics and AI capabilities.
- Apply advanced statistical techniques, machine learning algorithms, and experimentation methodologies to evaluate business opportunities, measure outcomes, and quantify business value.
- Lead the implementation and continuous improvement of enterprise AI, machine learning, and analytics platforms, standards, and best practices.
- Ensure compliance with enterprise data governance, privacy, security, responsible AI, and model risk management requirements.
- Contribute to the advancement of the Enterprise AI & Data CoE by evaluating emerging technologies and identifying opportunities to enhance analytical, machine learning, and AI capabilities across the organization.
- Communicate analytical findings, model performance, and AI solution outcomes effectively to both technical and non-technical stakeholders.
- Collaborate in an Agile delivery environment to support enterprise-wide data, analytics, automation, and AI initiatives.

What Skills and Qualifications Do I Need?

Education: Completion of a Degree in Statistics, Applied Math, Engineering, Computer Science, or a related discipline – or a combination of education, training, and experience deemed equivalent

Experience:

- Proven experience in Data Science, Advanced Analytics, Machine Learning, Predictive Modeling, or related disciplines.
- Master's degree and/or PhD in a relevant discipline (e.g., Data Science, Analytics, Computer Science, Statistics, Economics, Business, or a related

📌 Data Scientist II (Toronto)
🏢 Metrolinx
📍 Toronto