Find Data Scientist, ML Engineer, Data Engineer, Data Analyst, MLOps, Research Scientist, Analytics & AI Engineer roles
Discover and find all Data Scientist, ML Engineer, Data Engineer, Data Analyst, MLOps, Research Scientist, Analytics Engineer and AI Engineer roles.
On-site · Toronto
Co-op analysts are integrated into a Global Markets desk to develop business solutions, pricing models, and trading strategies. They are responsible for market data analysis, creating client presentation materials, and supporting trading and sales professionals.
Hybrid · Toronto
The role involves researching, building, and implementing scalable AI systems and deep learning models to meet business requirements. Responsibilities include optimizing data pipelines, running ML experiments, and developing tools to turn unstructured data into useful information.
Hybrid · Toronto
The role involves researching, building, and implementing scalable AI systems and deep learning models to meet business requirements. It also includes optimizing data pipelines, running ML experiments, and automating analysis into repeatable decision support tools.
Hybrid · Toronto
The role involves researching, building, and implementing scalable AI systems and optimizing data pipelines for machine learning models. Responsibilities include designing deep learning systems, running experiments, and developing ML apps to turn unstructured data into actionable business insights.
On-site · Toronto
The role involves using scientific methodologies and marketing analytics to optimize the customer experience and drive shareholder value. Key tasks include implementing MTA and MMM models to measure marketing channel effectiveness and providing value-added insights for website personalization.
Remote · Mississauga, Ontario, Canada, Salt Lake City, Utah, United States
The Principal AI Engineer will design and architect scalable, enterprise-grade AI/ML platforms and reference architectures for agentic systems and RAG pipelines. They will define technical standards, evaluate AI infrastructure vendors, and guide engineering teams in deploying cloud-native AI solutions.
Hybrid · Toronto
The role involves researching, building, and implementing scalable AI systems and optimizing data pipelines for machine learning models. Responsibilities include designing deep learning systems, running experiments, and developing ML apps to turn unstructured data into actionable business insights.
On-site · Toronto
Design and deploy production-grade Agentic AI systems and platforms to enable autonomous workflows across Investment Banking and Global Markets. Develop AI harnesses, evaluation frameworks, and secure inference pipelines while ensuring adherence to Responsible AI and governance principles.
On-site · Toronto
The Audit Manager will execute audits focused on quantitative modelling and analysis to strengthen the bank's risk framework. Responsibilities include assessing complex models, ensuring documentation compliance, and presenting findings to senior management.
Hybrid
Serve as the primary legal advisor for teams developing AI and machine learning models, focusing on agentic workflows and multimodal healthcare experiences. Identify and mitigate AI-specific legal risks while shaping the company's position on emerging AI regulations.
Remote · Canada
Guide product strategy by identifying growth opportunities and conducting exploratory analyses to increase user engagement and retention. Design business health metrics and drive the end-to-end experimentation process to inform future investment decisions.
On-site · Toronto
Develop and deploy state-of-the-art AI and ML models to improve the Depop marketplace experience for buyers and sellers. Build and scale large data pipelines and distributed systems while partnering across engineering and product teams to move solutions from experimentation to production.
Remote · Canada, United States
The Data Analyst will use AI-enabled tools and SQL/Python to generate insights that improve player engagement, retention, and monetization for Marvel Strike Force. They will own recurring business-health reporting and design analytics tracking for new features and experiments.
On-site · Toronto
Architect and develop high-performance real-time and batch data pipelines to power critical eBay experiences. Collaborate with Data Science and Product teams to translate complex requirements into scalable, reliable data solutions.
On-site · Toronto
The role involves architecting and developing scalable real-time and batch data pipelines using tools like Kafka, Spark, and Flink. The engineer will also manage workflow orchestration with Airflow and implement data quality frameworks to ensure high-fidelity data.
On-site · Toronto
Lead and develop a team of data engineers to build scalable real-time and batch data pipelines that power critical eBay experiences. Partner with cross-functional leaders to translate business priorities into high-quality data products and establish engineering best practices.
Remote · Canada
Develop analytical and financial models to quantify the business impact and GPU economics of AI capabilities across Autodesk's platform. Create strategic frameworks and forecasts for AI adoption, inference costs, and monetization to guide executive decision-making.
On-site · Toronto
The role involves analyzing and interpreting data to create actionable insights that drive business growth within Personal Banking. Key tasks include building data pipelines, developing machine learning models, and presenting findings to senior management.
Remote · Canada
Oversee the underwriting and credit risk validation sector by leading a team of quantitative professionals and setting strategic direction. Perform hands-on independent validations of complex machine learning models to ensure conceptual soundness and performance stability.
On-site · Toronto
The role provides first-line oversight for a portfolio of AI and machine learning initiatives, ensuring alignment with enterprise governance and risk standards. It involves coordinating between business, technology, and risk teams to streamline the AI lifecycle and deliver portfolio transparency to leadership.
