Top Analytics Jobs in Montreal

Lead end-to-end COGS and gross margin analysis across hardware and software product lines. Build scenario models, quantify cost-saving opportunities, and present defensible recommendations to C-suite. Tackle ambiguous executive questions, partner with data engineering, finance, and product to establish reliable datasets, and drive analytical best practices and documentation. Operate autonomously with high ownership.

Consult on data collection and analytics: gather client requirements, configure GA4/GTM (including server-side), build dashboards and automations, develop SQL/scripts, advise clients, train teams, and support sales estimations.

Lead people-analytics strategy for Talent Acquisition and Talent Development. Drive end-to-end analysis of complex workforce problems, deliver evidence-based recommendations to senior leaders, ensure data governance and quality, coach junior analysts, build metrics frameworks, and conduct benchmarking and external research to inform workforce strategy.

Partner with cross-functional teams to drive data-driven growth: design and analyze A/B tests, leverage ML models for in-depth analyses, define and test acquisition and partnership levers, and automate processes to improve operational efficiency and scalability.

Partner with cross-functional teams to drive data-driven growth: design and analyze A/B tests, define acquisition and partnership levers, leverage machine learning for insights, and automate processes to improve operational efficiency and scale customer acquisition.

The Senior Data Scientist will build frameworks for data-driven decision making, develop predictive models, ensure data quality, and enhance customer experiences through analytics and operational insights.

Lead growth and player behavior analytics across sportsbook, casino, and poker products. Partner with Revenue Ops, Marketing, Product, and VIP to run retention, bonus optimization, segmentation, funnel/cohort analyses, and A/B tests. Build predictive models and apply ML/AI tooling to generate actionable insights and recommendations that drive engagement, personalization, and ROI.

Perform hands-on product and player analytics across onboarding, retention, monetization, progression and LiveOps. Build and QA ETL pipelines in an AWS analytics stack, write cost- and performance-aware SQL, support A/B testing, and create reusable AI-enabled analytical workflows and notebooks to inform product, marketing, and LiveOps decisions.