Top Data Science Jobs in Toronto

The VP Global Grid Modeling will lead global data strategy, manage high-performing teams, ensure quality datasets, and engage with customers for insights in energy markets.

As an Associate Product Manager at Switch, you will drive customer success, manage software implementation, and collaborate with engineers while shaping the product roadmap based on customer feedback and analytics.

Lead product strategy and delivery for AIRCHECK, coordinating multidisciplinary teams to build a scalable, secure, and reproducible AI-ready chemical screening platform. Define roadmaps, agile processes, priorities, and milestones; oversee data ingestion, curation, APIs, UI, cloud infrastructure, MLOps, documentation, and user engagement. Bridge scientific goals and technical implementation while managing risks and fostering an open-science culture.

As a founding Data Scientist/Machine Learning Engineer, you'll develop AI/ML models, enhance product capability, and drive impactful user outcomes while working closely with product teams.

Lead roadmap and deliverables for measurement products (attribution, ABM, first-party data), create 0→1 solutions, write clear product specs, and drive features to completion working with engineers, designers, QA, and cross-functional teams.

Lead design and delivery of ML and AI solutions by mining large datasets, building predictive models, and collaborating with product and analytics teams to drive data-driven business decisions and improve customer experience. Provide technical consulting, scale analytics solutions, and apply risk-aware decision making.

The Forward Deployment Engineer will deploy and optimize Generative AI solutions, architect scalable infrastructure, and implement data pipelines across multi-cloud environments while collaborating with cross-functional teams to maximize business value.

Lead end-to-end delivery of data science, ML, and AI programs from scoping to production. Build roadmaps, manage cross-functional resourcing, run Agile ceremonies, manage risks/dependencies, coordinate model lifecycle and data engineering readiness, oversee executive reporting, and mentor PMs and technical leads to establish delivery best practices.

Lead and scale the Growth engineering organization at MaintainX, focusing on technical strategy, product-led growth, and enhancing cross-functional initiatives.

Lead and scale a product-focused Data Science team to drive acquisition, engagement, retention, and member health outcomes. Set DS strategy, measurement frameworks, operating model, and prioritize high-impact analytics. Partner cross-functionally, ensure rigorous, actionable analyses, apply experimentation, causal inference, segmentation, forecasting and personalization, and collaborate with data engineering to improve data foundations.

Lead and build a hands-on data science and analytics function for a live-service gaming company. Own analysis (SQL, Python/R), dashboards, KPIs, experimentation, data quality, pipelines, and executive reporting. Partner across product, ops, marketing, finance and engineering. Implement AI-driven analytics, automation, and copilots, then hire and scale a high-performing team while establishing governance and responsible AI practices.

Provide analytics support to business lines and finance through data analysis, automated workflows, BI reporting, advanced analytics and generative AI. Engage stakeholders, present findings to senior leaders, improve processes, and implement cloud-based data solutions.

Lead design and execution of bioinformatics analyses for cancer genomic and single-cell/spatial datasets. Develop and maintain high-throughput pipelines, integrate multi-omics data, support collaborators, manage compute resources, contribute to open-source code, prepare manuscripts and presentations, and mentor trainees.

Lead the PoC data science team, delivering POCs for enterprise clients, mentoring team members, and driving fraud analytics and ML solutions.

Lead end-to-end analytics and AI/ML initiatives: diagnose performance across customer/product/advisor dimensions, build scalable datasets, dashboards, segmentation, and predictive models, implement cloud-based ML solutions, produce executive-ready visualizations and stories, mentor teams, and drive adoption of advanced analytics across business stakeholders.

Lead and scale a product-focused data science organization to build production ML systems for personalization, recommendation, experimentation, and causal inference. Define strategy, partner with Product and Engineering, own end-to-end ML lifecycle, mentor the team, and measure product impact through robust analytics and monitoring.

Lead the development and delivery of AI-driven product capabilities, transforming data into actionable insights while mentoring a high-performing team of data scientists and engineers.

Evaluate and label model-generated and human-written code outputs, debug and assess code/agent trajectories, prompt models for complex data science tasks, review visualizations, and report quality trends to improve model behavior.

Lead and mentor a data science team to enhance the Rider App experience through machine learning, analytics, and experimentation. Partner with stakeholders to drive data-driven decisions and innovation.

Design and maintain ETL/ELT pipelines and semantic/analytical data models; extend metric layers for BI and LLM/RAG-driven natural-language analytics; apply AI/ML and data-science techniques to produce trusted investment metrics, insights, and recommendations; embed data quality, lineage, and governance; collaborate with investment, finance, and technology stakeholders to prototype and productionize AI-enabled reporting solutions.