Top Data Jobs in Toronto

The Data Scientist 2 will engage in the full data science lifecycle, including data extraction, model development, and deployment to meet business needs.

The Director of Data Engineering leads the enterprise data platform strategy, ensuring scalability and reliability across business units, while enhancing data practices and overseeing reporting platforms for effective decision-making.

The Senior Data Scientist will optimize algorithms and employ advanced analytics for pricing in the supply chain, collaborating with teams and driving innovation.

As a Data Scientist II, you will build and deliver data science solutions, validate models, support teams, and communicate insights to stakeholders.

Build, own, and evolve Flinks' data and ML platform: BigQuery/dbt pipelines, Airflow ingestion, Kubeflow/Vertex training, model serving, governance, observability, and cross-team data contracts to support production ML and analytics at scale.

Own and ship production ML models end-to-end: problem framing, model design, training pipelines, deployment to served endpoints, and model-quality monitoring. Design experiments, detect drift, run champion-challenger evaluations, and partner with Data Engineering, backend, product and QA to integrate model outputs that drive business outcomes in fintech.

Lead design, development, testing, implementation and support of data and AI products. Translate business requirements, provide technical direction, mentor developers, build automation and frameworks, improve delivery processes, and perform data analysis, profiling and regression testing.

Implement and configure enterprise Workday HCM solutions, lead the data workstream including gathering, transforming and loading organizational and employee data, liaise with customers and internal stakeholders, deliver data solutions, support sales and practice improvements, and educate others within the Workday practice.

Lead design, development, and maintenance of cloud-based marketing data products and complex data pipelines. Collaborate with marketing, LOB data experts, and consultants to deliver data assets, enable cloud migrations, and support marketing analytics users.

Design, develop, and deliver data science solutions and enterprise BI assets. Collaborate with stakeholders and data engineers to build Databricks/Azure-backed pipelines, PySpark/Python data preparation, and Power BI/Tableau dashboards to enable data-driven decisions and self-serve analytics.

Partner with customers to scope and deliver custom data requests, conduct Business Value Analyses, lead API integrations, build internal data tools and automations, produce executive-ready insights, and mentor analysts while collaborating cross-functionally to scale data solutions.

The Data Scientist role involves developing and deploying machine learning models for fraud detection, transforming datasets into insights, and collaborating across teams. Responsibilities include building data pipelines and ensuring model performance.

Execute client data management, extraction, analysis, reporting, dashboarding and process automation to support Client Data Strategy, regulatory reporting and business intelligence. Partner with Technology and stakeholders to implement tools, support regulatory remediation, provide project management support, and improve operational efficiency.

Partner with business, IT, and analytics teams to define and enforce data standards, improve data quality, investigate data issues, and govern large datasets. Use SQL and BI tools and data catalogues to document metadata, shape enterprise-wide governance practices, and influence stakeholders to adopt consistent data management practices.

Lead Canadian Banking data governance: define and enforce policies, improve data quality and protection, manage stewardship roles and artifacts, report metrics, oversee remediation, and liaise between business, technology, and senior management to implement a governance framework and ensure compliance.

Lead implementation projects focused on data management and integration for financial institutions. Define solutions using SimCorp One®, liaise with clients, coordinate with project managers, mentor colleagues, improve implementation best practices, and ensure delivery of data integration and performance improvements.

Analyze and report sales, pipeline, inventory, and revenue data to identify growth opportunities, support planning, build BI reports, aid quota determination, and publish sales intelligence to teams.

Lead design, build, and operate scalable Generative AI systems (LLM apps, RAG pipelines, multi-agent systems) in production using Databricks and cloud infrastructure. Own CI/CD, monitoring, evaluation frameworks, optimize retrieval/generation, mentor engineers, and influence product roadmap.

The Marketing Data Analyst will design data models, implement analytics solutions, and deliver insights for marketing optimization. They will work across channels and support data-driven decision-making by partnering with teams and stakeholders, leveraging AI tools for efficient analytics.

The Director of Product Management will lead product strategy across infrastructure, software enablement, and customer-facing platforms, enhancing both technical and user experience while managing a team of product managers.

In this role, you will define and execute the go-to-market strategy, drive growth, and lead customer success efforts at Reflow, a workplace intelligence platform.

As a Product Manager at Reflow, you will lead product strategy, execution, and delivery while ensuring customer engagement and managing engineering dependencies in an early-stage startup environment.

Develop and extend data ingestion pipelines for AI model training, manage cloud infrastructure, and collaborate with AI scientists. Bring new audio data sources into the system.

Lead design, development, and deployment of data science and GenAI solutions. Translate business goals into ML problems, engineer features, train/evaluate models and LLMs, build RAG/agent systems, work with vector DBs, mentor juniors, and communicate results to stakeholders.

Design, model and implement enterprise data integration and ETL solutions using Oracle Hyperion Planning, Essbase, Oracle BI and Oracle Data Integrator. Analyze and cleanse source data, build automated extraction/transform/load processes, scheduling, monitoring and error notification. Provide full life-cycle data conversion, reporting, and automated distribution for centralized financial planning and budgeting systems. Work with business and IT stakeholders to design resilient, multi-tier solutions aligned with Government of Ontario IT standards.