Top Data Science Jobs in Montreal

Supports the design, documentation, validation, and implementation of derivatives and market risk models. Responsibilities include developing methodologies for VaR, stress testing, and regulatory frameworks such as FRTB, SIMM, and ICAAP; reviewing model inputs; implementing products in risk systems; participating in requirements workshops; and contributing to exploratory risk modeling initiatives.

Own end-to-end data science for growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; communicate insights; and drive data-informed decisions and measurable business outcomes with cross-functional stakeholders.

Own end-to-end data science for growth, product, AI and automation, or strategic partnerships. Define business questions, metrics, reports, analyses, and experiments; generate actionable insights; and influence product, growth, finance, and operations leaders. The role requires independent problem solving, strong SQL or Python skills, clear communication, and ownership of ambiguous, high-impact initiatives.

Owns end-to-end data science for growth, product, AI and automation, or strategic partnerships. Responsibilities include defining business problems, developing metrics and reports, analyzing data, designing experiments, communicating insights, influencing leadership decisions, and driving measurable business outcomes. Requires strong SQL or Python skills, independent analytical ability, comfort with ambiguity, persuasive communication, and ownership of strategic initiatives.

Own end-to-end data science for growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; communicate insights and recommendations; and drive decisions and measurable outcomes with cross-functional stakeholders. The role requires strong analytical thinking, independent data work using SQL or Python, persuasive communication, and ownership of ambiguous business problems.

Develop data science solutions for payments across Square and Cash App. Build AI-powered tools, ETL pipelines, dashboards, machine learning models, experiments, and cloud data systems. Partner with Product, Engineering, Finance, and Operations to improve payment success, cost efficiency, risk mitigation, and decision-making. Translate business needs into practical solutions, research questions independently, and operationalize data processes and infrastructure.

Develop data tools, AI automations, ETL pipelines, dashboards, and machine learning models for payments. Analyze experiments, optimize payment performance and costs, mitigate risk, maintain data systems and cloud infrastructure, and translate business needs into actionable solutions while partnering with Product, Engineering, Finance, and Operations.

Leads a small data science team while remaining hands-on in building advertising audience, measurement, ranking, and data-feed models and systems. Owns technical direction, causal inference frameworks, predictive modeling, experimentation, and productionization. Mentors scientists, partners with product, engineering, sales, and business development, communicates methodology to customers and executives, and establishes scalable standards for measuring advertising impact.

Lead data science strategy for Fetch’s Play and Rewarded Apps business. The role develops predictive and machine learning models, designs experiments and causal analyses, builds scalable analytical workflows, and connects user behavior to revenue, engagement, retention, and marketplace outcomes. This senior individual contributor partners with Product, Marketing, Engineering, and executives, operationalizes analytical solutions, communicates recommendations, mentors data scientists, and establishes best practices for rigorous modeling, experimentation, documentation, and data storytelling.

Develops, monitors, and maintains credit risk models supporting risk quantification and credit decisions. Designs mathematical models, ensures compliance with business and regulatory requirements, leads modeling initiatives, advises practitioners, communicates complex information, and monitors industry trends. Requires expertise in credit risk, advanced statistics, Basel and IFRS standards, and SAS, SQL, and Python.

Develops and deploys data science models, rules, and strategies to detect transactional fraud. Analyzes fraud patterns, researches predictive variables, tests and monitors model performance, and automates fraud analytics processes. Creates dashboards and reports, advises risk management teams, and tracks industry trends. Requires experience with transactional analytics, cloud platforms, Python or R, SQL, Apache Spark, Databricks, and data science libraries.

Leads company-wide measurement and causal strategy, defining canonical metrics, semantic data architecture, experimentation standards, and strategic models such as marketing mix, elasticity, incentive sensitivity, and forecasting. Partners with engineering and analytics teams to build scalable, production-ready analytical frameworks, improve scientific rigor, and guide executive decisions. Provides organization-wide technical leadership, develops modeling standards, and applies advanced statistical and causal methods to high-impact business problems.

Build and deploy end-to-end statistical and machine learning solutions for clients and internal delivery teams. Responsibilities include scoping ambiguous problems, analyzing operational challenges, prototyping AI workflows, translating messy data and real-world constraints into scalable solutions, iterating with stakeholders, and developing reusable tooling and infrastructure. The role combines hands-on data science with technical consulting and requires communication with technical and non-technical stakeholders.

Build and scale first-line risk control monitoring systems using statistical analysis, automated workflows, and reusable data science modules. Define risk metrics, investigate control performance, establish thresholds and guardrails, and translate analyses into monitoring applications. Partner with risk, compliance, product, and engineering teams to improve governance, identify monitoring gaps, and support decision-making. Lead ambiguous, cross-functional initiatives while communicating findings, limitations, and recommendations to technical and executive stakeholders.

Develop end-to-end data analytics solutions for supply chain planning and optimization. Responsibilities include gathering requirements, building data pipelines, dashboards, algorithms, reports, and performance indicators using SQL, Python, PySpark, and Power BI. The role contributes to AI, LLM, RAG, operations research, data architecture, data quality, and SAP ERP initiatives while supporting executive decision-making and training end users. This is an exclusively on-site position in Longueuil, Quebec.

Build and scale first-line risk control performance monitoring for financial products. Develop metrics, key risk indicators, statistical baselines, thresholds, automated workflows, and reusable analytical modules. Partner with risk, compliance, product, and engineering teams to improve governance, identify monitoring gaps, evaluate control effectiveness, and support decisions. Lead ambiguous initiatives from definition through adoption while communicating findings, limitations, and recommendations to technical and executive stakeholders.

Own product experimentation and causal analysis for growth and engagement initiatives. Define KPI frameworks, evaluate retention and user behavior, build predictive models for lifecycle interventions, and assess segmented and long-term effects. Partner with Product, Engineering, and Design to recommend launches, iterations, or stops. Translate ambiguous questions into actionable insights, influence product priorities, and create reusable analyses using Python, SQL, Snowflake, dbt, Airflow, Spark, and AWS.

Own company-wide KPI, modeling, and measurement workstreams. Build trusted metric frameworks, KPI trees, forecasts, scenarios, causal analyses, and reproducible SQL/Python workflows connecting product drivers to retention, monetization, revenue, and margin. Partner with Data Engineering, Product, Engineering, Finance, and Commercial teams to define certified metrics, build analytical assets, drive adoption, and communicate assumptions, sensitivity, uncertainty, and actionable recommendations.

Leads company-wide data science priorities, KPI frameworks, measurement standards, causal modeling, scenario planning, and agentic analytical automation. Builds reproducible SQL and Python workflows, validates predictive and causal relationships, establishes governed self-service analytics, and partners with Finance, Product, Commercial, Engineering, and Data Engineering. The role drives trusted metric adoption, improves decision speed, mentors scientists, and aligns leadership on targets, trade-offs, uncertainty, and investment decisions.

Own the scientific and product roadmap for Fetch’s monetization data products, including audience creation, expansion, causal measurement, and audience improvement. Lead methodology, evaluate evidence, balance reach, precision, revenue, and cost, and convert bespoke studies into scalable products. Partner with Product, Engineering, Sales, and GTM on pilots, positioning, and customer value. Remain hands-on with SQL, Python, statistical modeling, and causal inference while mentoring scientists and influencing senior stakeholders.

Own audience or measurement products for Fetch’s monetization business, from problem framing through delivery. Build predictive models, datasets, population inferences, and causal measurement studies using receipt, partner, panel, survey, and contextual data. Partner with Product, Engineering, Sales, and GTM to productionize analyses, communicate methods and limitations to customers, and improve products using campaign outcomes, holdouts, and fresh data.

Own the data science strategy, analytical roadmap, and experimentation for ranking and relevance. Partner with engineering, product, commercial, and offer teams to improve advertiser outcomes, user value, retention, and marketplace economics. Design rigorous experiments and observational analyses, quantify incremental business impact, establish KPI frameworks and trade-off guardrails, influence launch and investment decisions, mentor colleagues, and build reusable analytical methods using large-scale data systems.

Lead end-to-end data science and machine learning projects, from scoping ambiguous client problems through production deployment. Build and evaluate statistical and ML models, analyze large datasets, run experiments, collaborate with engineers, create reusable tooling, communicate findings to diverse stakeholders, and mentor data scientists. The role combines hands-on data science with technical consulting and requires occasional customer on-call support.

Develop machine learning and AI security solutions to detect anomalous activity, abuse, fraud, and security risks. Analyze large behavioral datasets, build and improve detection models, evaluate Zendesk AI features, and guide production implementation. Collaborate with security, engineering, research, and product teams; mentor colleagues; monitor model performance; and translate technical findings into actionable prevention strategies.

Lead pricing modeling and experimentation to optimize margin, conversion, and retention. Build elasticity and willingness-to-pay models, design A/B and bandit experiments, develop pricing automation (DealBot) and monitoring for AI-driven rate recommendations, own ETL/analysis/visualization, and partner with Finance, Risk, Product, and GTM teams to operationalize pricing intelligence.