Top Data Science Jobs in Toronto

Lead the Fraud and Ad Quality product portfolio, defining strategy, roadmap, go-to-market, and execution for ad fraud detection, invalid traffic measurement, and content quality solutions. Partner with Engineering, Data Science, Business Development, Commercial, and Product Marketing teams to guide architecture, prioritize initiatives, drive adoption, and deliver innovative ML/AI-enabled products for digital advertising customers.

Leads product strategy and development for Mastercard’s Business Identity solutions supporting agentic commerce. Translates strategic goals into roadmaps, gathers customer and stakeholder requirements, prioritizes features, and coordinates engineering, data science, legal, and commercialization teams. Manages product launches, lifecycle planning, performance tracking, and continuous improvement while advancing merchant verification, risk intelligence, fraud mitigation, regulatory compliance, and digital trust. Mentors product managers and drives measurable customer and market impact.

Leads product management for identity and token intelligence solutions that prevent fraud in digital payment ecosystems. Responsibilities include identifying customer and fraud use cases, defining product features, prioritizing roadmaps, analyzing product and model performance, coordinating with engineering and data science, and supporting commercialization through marketing and sales enablement.

A 12-week unpaid fellowship providing hands-on training in Palantir Foundry and AIP. Participants attend live virtual instruction, complete technical projects, engage with Palantir engineers and leadership, and earn a professional certification. The program seeks builders with technical experience in software, data, automation, process improvement, prototyping, or related fields. Exceptional performers may interview for roles at Palantir or partner companies.

Develop and optimize AI and generative AI models, collaborate with data science, engineering, and product leaders, and rotate across AI-focused teams. Contribute to strategic AI initiatives, platform enablement, innovation projects, and TLAP community activities while receiving mentorship and professional development support.

Leads GitLab’s Product, Sales, and Finance Data Science teams, managing managers and a 12-person organization. Sets data science standards, prioritizes investments, partners with executives, drives product adoption and revenue growth, and oversees experimentation, causal inference, predictive modeling, and AI initiatives. Develops talent, builds succession, and collaborates with Analytics Engineering and Data Engineering on a shared data and AI roadmap.

Design and evolve Forum’s enterprise data platform using Microsoft Fabric and Azure services. Build scalable ETL/ELT pipelines, lakehouse architecture, integrations, APIs, and self-service analytics tools. Develop AI-powered workflows using Azure OpenAI, Document Intelligence, and Azure ML. Partner with investment, real estate, and operations teams to transform complex data into business intelligence, enable automation, and establish data governance and AI best practices.

Leads cross-Risk and cross-Stripe product initiatives, shaping technical and product direction for risk infrastructure and experiences. Identifies ambiguous, high-impact problems; partners with engineering, data science, and machine learning leaders; guides architecture, data foundations, models, APIs, and adoption. Builds alignment across teams, drives execution without formal authority, and advances AI-driven approaches to fraud, credit, financial crime, and broader risk management.

Leads data science and pricing-modeling teams, overseeing machine learning, predictive analytics, model governance, and scalable data-driven solutions. Partners with Pricing, Actuarial, Risk, Technology, and Engineering teams to improve business outcomes and customer experiences. The role includes translating analytical insights into decisions, driving innovation and adoption, allocating resources, and developing high-performing technical leaders.

Leads GitLab’s Product Data Science team, setting its strategy, roadmap, analytical standards, and priorities. Partners with Product and Engineering leaders on experimentation, causal inference, analytics, and production modeling. Manages, hires, coaches, and retains data scientists while building a high-performing function. Provides hands-on technical review of models, analyses, and queries, and communicates insights and risks to senior leaders.

Lead end-to-end delivery of enterprise Generative AI and Agentic AI programs, coordinating data science, machine learning, data engineering, product, architecture, and client teams. Oversee RAG pipelines, multi-agent ecosystems, knowledge graphs, production deployment, risks, dependencies, and technical challenges. Communicate program progress, blockers, and decisions to senior leadership and stakeholders while ensuring successful delivery of complex AI initiatives.

Leads a team of Product Sales Specialists responsible for product adoption, cross-sell, expansion, pipeline, and revenue outcomes. Coaches consultative selling, manages forecasting and performance, develops scalable sales plays, and partners with Account Executives, Account Managers, Product, Marketing, Operations, Data Science, and Enablement. The role also engages in strategic seller opportunities, improves product expertise across sales teams, and builds repeatable commercial motions for Square’s products.

Lead eBay’s data science and analytics function by setting strategic roadmaps, developing AI-enhanced metrics, predictive models, experimentation frameworks, dashboards, and growth solutions. Partner with senior business and product leaders to influence decisions, improve operational efficiency, and deliver measurable impact. Manage, mentor, recruit, and develop analytics talent while promoting an AI-first, innovative team culture.

Lead end-to-end executive and leadership recruiting across business and technology functions, including talent mapping, sourcing, stakeholder partnership, hiring strategy, offer negotiation, and closure. Mentor recruiters, monitor pipelines and hiring metrics, and develop talent strategies for data science, analytics, data engineering, and machine learning roles. The position partners with senior business leaders and supports recruiting growth in a global analytics consulting environment.

Own the acquisition product roadmap and engineering priorities to drive durable user growth. Define target audiences and channel mix, build product-driven acquisition mechanics (referrals, growth loops), enable channel effectiveness (discoverability, search/AI readiness, integrations, tracking), test new acquisition tactics, and align product investment with Performance Marketing, SEO, Partnerships, and Brand while measuring long-term subscriber value.

Leads data science and marketing science strategy across Marketing and Product. Owns marketing measurement through media mix modeling, incrementality testing, geo-testing, and attribution. Builds predictive models for lifetime value, propensity, churn, and retention; establishes production standards and governance; writes and reviews Python, SQL, and dbt code; manages external measurement partners; and mentors data scientists while serving as the organization’s statistical authority.

Lead data science initiatives for agentic AI products, partnering with data scientists, AI/ML engineers, and product managers. Build, deploy, validate, and optimize machine learning models using large-scale numeric and textual data. Design A/B tests, analyze telemetry, translate technical findings into business strategy, research emerging technologies, and develop talent while guiding enterprise-scale AI decisions.

Lead technical strategy and pre-sales engagement for large enterprise accounts, guiding customers through Databricks adoption and data-driven transformation. Build executive relationships, present technical value propositions, coordinate virtual teams, develop technical champions, align account strategies, and mentor colleagues. The role requires expertise in big data, data science, cloud, ML and AI use cases, plus production programming experience in Python, R, Scala, or Java.

Lead technical strategy and adoption of Databricks solutions for large financial services and insurance accounts. Partner with enterprise sales teams, executives, influencers, and technical resources to drive data and AI transformation, build technical champions, guide evaluations, and align solutions with business outcomes. The role requires expertise in big data, data science, cloud platforms, machine learning, programming, value selling, and technical leadership across global enterprise accounts.

Leads enterprise HR analytics and AI consulting engagements from discovery through delivery. Translates business challenges into scalable analytics solutions, manages senior client stakeholders, and guides cross-functional data science, data engineering, and business analyst teams. Oversees HR data transformation initiatives, advises on workforce metrics, supports embedding analytics into business processes, and identifies opportunities for solution pitches and account expansion.

Lead analytics consulting engagements for enterprise clients by translating business challenges into AI and analytics solutions. Define agentic AI requirements, workflows, and outcomes; oversee end-to-end delivery; collaborate with technical and global teams; manage stakeholders and projects; apply statistical and machine learning techniques; and help operationalize solutions that deliver measurable business value.

Leads a data science organization delivering predictive, prescriptive, and generative AI solutions. Responsibilities include managing and developing data scientists, prioritizing analytics opportunities, overseeing model development and deployment, establishing responsible AI and governance practices, partnering with technology and business stakeholders, presenting insights to executives, and managing portfolios, resources, risk, and delivery across multiple initiatives.

Leads and mentors a data science team developing machine learning, AI, predictive, prescriptive, and generative AI solutions. Partners with business stakeholders to prioritize use cases, oversees model development and production deployment, and ensures governance, monitoring, documentation, explainability, fairness, and responsible AI standards. The role also communicates analytical insights, evaluates emerging technologies, manages team performance and resources, and supports enterprise risk, regulatory, and strategic initiatives.

Develop enterprise customers’ automation strategies, identify and deploy high-value AI-enabled use cases, quantify ROI, and drive adoption, renewal, and expansion. Build executive and stakeholder relationships, advise on governance and scalable orchestration, coordinate internal and customer teams, interpret usage data, and create repeatable methodologies and playbooks for Zapier’s enterprise customer success program.

Designs challenging competitive data science and machine learning tasks, reproducible execution environments, reference solutions, automated tests, and evaluation criteria. Uses Python, statistical modeling, mathematical reasoning, and debugging to assess AI coding agents, identify data leakage and implementation flaws, validate solution correctness, and document methodologies. Requires a verifiable Kaggle Expert-level profile, strong practical programming skills, and the ability to independently test and explain technical solutions.