Top Data Jobs in Toronto

Develop and implement AI risk assessment and pricing frameworks by evaluating AI models, creating rating tables and pricing models, collaborating with underwriters, building Python-based analytical tools, and researching emerging AI risks and mitigation strategies.

Lead design and implementation of a federated Lakehouse data platform, unify data silos, define governance/observability, enable secure AI/ML (RAG/vector readiness), mentor architects/engineers, and present roadmaps to executives.

The Data Engineer is responsible for designing and developing cloud-native data pipelines, integrating machine learning models, and ensuring the reliability and scalability of data solutions in a collaborative environment.

The Data Engineer will analyze and interpret data to drive insights, create dashboards, and enhance product performance, collaborating with cross-functional teams.

As a Data Analyst, you will analyze user behavior and product performance, creating reports and dashboards to aid decision-making and optimize user experience.

The Product Analyst will oversee product analytics, collaborate with teams, define metrics, build dashboards, and improve data quality in a hybrid work environment.

Senior Business Data Scientist, Marketing partners with cross-functional leaders to drive customer analytics across acquisition, onboarding, engagement, retention, monetization, and expansion. Lead deep-dive analyses, experimentation and impact evaluation, KPI governance, forecasting, and predictive analytics. Translate complex business questions into actionable recommendations, build reusable decision frameworks and scalable analytics assets, mentor analysts, and communicate executive-ready insights to influence strategic priorities and improve long-term customer value.

Own Zip's account universe, territory carving methodology, and enrichment strategy. Manage data quality across Salesforce, run annual data refreshes with vendors, build GTM analytics, automate workflows with AI, and partner with sales and Business Systems to execute territory and routing changes.

The Senior Data Scientist will drive the development and implementation of advanced statistical methodologies, lead client consultations, and manage complex analyses, while providing innovative analytical solutions and mentoring team members.

Design, build, and maintain scalable data infrastructure and ETL pipelines using Python, SQL, PySpark, and Microsoft Fabric. Collaborate with Analytics and Data Science to translate requirements, optimize queries, investigate and resolve data issues, and support reliable pipeline operations and system architecture.

Lead development of advanced ML/AI solutions: design large-scale data pipelines, build and deploy ML/deep learning and LLM models, collaborate with product and analytics teams, deliver data-driven recommendations, and scale MLOps processes.

Looking for a Data Engineer experienced in Dataiku to design and maintain data pipelines and infrastructure, ensuring data flow and integration across teams.

The Data Engineer will architect and implement advanced analytics capabilities, manage data pipelines, and optimize data processing using AWS cloud and various analytical tools.

Design, build, and maintain reliable, scalable ETL/ELT pipelines, support data migrations, prepare analytics and AI-ready datasets, implement data quality and observability, support Power BI models, and collaborate with analytics, IT, governance, and AI teams to deliver production data solutions.

The Lead Data Scientist designs and implements data-driven solutions, leads a team, collaborates with cross-functional teams, and mentors junior scientists while staying updated with industry advancements.

As a Staff Data Scientist, lead the development of a decision intelligence product integrating multifaceted R&D data to generate actionable insights using advanced AI and ML techniques.

Partner with the Self-Serve product team to measure and optimize the acquisition-to-engagement funnel. Build reusable datasets and dashboards, validate instrumentation, run deep-dive analyses and experiments, and translate insights into narratives for technical and non-technical stakeholders to inform product and go-to-market strategy.

Lead the technical delivery and design of scalable kdb+ solutions while mentoring junior developers and collaborating with financial analysts.

Collaborate with business users, develop and maintain code for applications, analyze time-series data, and mentor junior developers.

The Lead Data AI Architect leads the design and implementation of secure data and AI architectures, guiding teams on best practices and managing compliance with business strategies and technical standards.

Lead strategy and roadmap for Wattpad's data platform and product intelligence. Build systems that transform raw events into reusable data products, trusted metrics, and decisioning services. Partner with Data Engineering, Data Science, Analytics, and Product to enable personalization, recommendations, experimentation, and governance. Establish shared definitions, reduce duplication through reusable capabilities, and balance long-term platform investments with near-term business priorities.

Build and operate end-to-end time-series forecasting and MLOps systems for battery energy storage: data pipelines, feature engineering, model training (including Temporal Fusion Transformer), deployment, monitoring, and production support. Create Airflow DAGs, ensure pipeline reliability, collaborate cross-functionally, and produce dashboards and documentation for stakeholders.

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.

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.

Develop and deploy data-driven strategies and machine learning models for Life & Health insurance using SQL and Python. Analyze data, automate processes, present insights to technical and non-technical stakeholders, and explore AI opportunities to improve decision-making.