Top Data Jobs in Toronto

Design and deploy predictive models and segmentations using statistical and ML/AI methods. Prepare, clean, and feature-engineer data from warehouses, develop and operationalize models, create visualizations and presentations, document rules, and advise stakeholders to inform business decisions and improve data management tools.

Audit and ensure quality of data labeling work for ML using human-in-the-loop processes. Provide feedback, train and support annotators, monitor workflows, identify recurring issues, and help improve guidelines and processes.

Evaluate AI model outputs, perform audit-style reviews to find patterns and failure modes, apply and refine labeling/evaluation guidelines, document findings, communicate insights to leads and stakeholders, and participate in team calibrations to improve model evaluation and data quality.

Evaluate AI outputs, perform audit-based reviews, identify patterns and failure modes, apply and improve annotation/evaluation guidelines, document findings, and communicate insights to cross-functional teams to improve model performance and evaluation frameworks.

Evaluate AI model outputs, perform audit-based reviews to identify patterns and failure modes, apply and refine guidelines, document findings, and collaborate with leads and cross-functional teams to improve model evaluation and data quality.

Support sales operations through weekly/monthly/quarterly reporting and forecasting, track field sales bonuses and retailer contests, assist traffic reporting and vehicle purchase planning, manage Sales Ops inbox and interdepartmental requests, help coordinate events, and drive automation projects using Power BI and advanced Excel.

Build and maintain scalable, reliable data systems and pipelines, uphold SLOs, partner with Product/Sales/Finance, enable self-service analytics, contribute to data platform strategy, and improve engineering practices for testing, reliability, and documentation.

As a Data Scientist at Sardine, you'll design and deploy data-driven solutions to prevent fraud, collaborating with clients and engineering teams to build and optimize machine learning models.

Use advanced analytics to monitor collections performance, evaluate third-party agencies, design incentive programs, and develop Power BI dashboards. Partner with operations, strategy, and risk to translate insights into actions, document data flows, and deliver training to non-analytical users to improve collections efficiency and portfolio outcomes.

As a Software Engineer 2 - Data Acquisition, you'll design and build data ingestion pipelines, ensuring quality and performance across multiple data domains while participating in code reviews and collaborating with data consumers.

Lead and grow a small data team while remaining hands-on in data engineering, analytics engineering, and infrastructure. Own data strategy, warehouse architecture, pipelines, modeling, and self-serve analytics. Partner cross-functionally to deliver trusted metrics, experimentation, and applied ML capabilities (search, recommendations, personalization). Drive adoption of data quality, documentation, self-service, and modern data practices.

Lead and execute end-to-end Salesforce data migration projects: define strategy, design and validate data models, build ETL pipelines, resolve data quality issues, perform reconciliation and validation, document mappings, and coordinate cross-functional teams to ensure compliant, accurate migrations on schedule.

The Data Engineer is responsible for designing and optimizing data pipelines, ensuring data integration, and collaborating with teams on AI-driven applications.

As a Software Engineer in Data Infrastructure, you will build and maintain high-performance data storage systems for AI training, collaborate with experts, and optimize data processing.

The Senior Data Engineer designs, builds, and maintains data pipelines and ensures data quality controls while supporting data analytics and reporting needs.

As a Workday Financials Data Lead Consultant, you will implement and configure Workday solutions, lead data workstreams, and support customer relationships while driving data consulting growth within Kainos.

Lead ML/NLP work across recommender systems, taxonomies, and agentic AI solutions. Prototype models, advise on data science strategy, mentor teammates, and deliver production-ready, well-documented ML pipelines and insights.

Build and maintain BigQuery data pipelines, map warehouse schemas, and craft SQL to feed LLMs. Develop Claude Skills and Gemini Gems, collaborate on prompts, validate AI outputs, and design AI agents to automate eCommerce analytics and operational workflows.

Build and maintain analytics platform, dashboards, KPIs and data models. Perform data cleaning, troubleshooting, ad-hoc analysis, and partner with operations, product, and engineering. Spend time onsite in warehouses to inform metrics and reporting.

Design, build, and maintain scalable batch and streaming data pipelines and domain-oriented data models using dbt and cloud warehouses. Ensure data quality, documentation, and usability for analytics, ML, and reporting while collaborating with Product, Analytics, and ML teams.

Lead development and maintenance of finance BI dashboards and analytical tools to deliver actionable insights. Partner with Finance and Data teams to prioritize DA&I projects, automate management reporting, support OneFinance consolidation, maintain enterprise data dictionary (Collibra), and drive process improvements and analytical capability development for decision makers.

Design, build, and maintain robust terabyte-scale data pipelines and stream-processing applications. Collaborate with product teams, participate in full SDLC, automate tests and deployments, monitor operations, and contribute to technical strategy and continuous improvement. Participate in on-call rotation.

As a Data Scientist, develop machine learning models to support trading strategies, collaborate with teams, analyze data, and improve trading performance.

The Vice President, Data Scientist will develop and implement machine learning models to enhance algorithmic trading strategies and provide data-driven insights for Investment Banking and Corporate Banking.

Build and deploy data science models and pipelines to generate insights for Sales, Marketing, Finance, and HR. Perform data preprocessing, feature engineering, model selection and evaluation, create dashboards and reports, use cloud data platforms, and communicate findings to technical and non-technical stakeholders.