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.

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.

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.

Analyzes usage data across 15+ web applications, builds data pipelines, develops predictive models, and introduces AI/ML tools to improve analytics. Creates executive dashboards and reports, identifies trends and anomalies, communicates actionable recommendations, and ensures data integrity, security, and compliance. Collaborates with technical teams and business stakeholders while documenting analytical methodologies and presenting findings to leadership.

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.

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.

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.

Own end-to-end analytical functions for growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; surface insights; and influence product, growth, finance, and operations decisions. The role requires strong independent data analysis, first-principles problem solving, clear communication, and ownership of ambiguous business challenges.

Own the analytical function for a business area such as growth, product, AI and automation, or strategic partnerships. Define business questions and metrics, build reports, analyze data, design experiments, communicate insights, and drive decisions and outcomes with cross-functional stakeholders. The role requires strong analytical thinking, independent problem-solving, persuasive communication, and end-to-end ownership of ambiguous business challenges.

Own end-to-end analytical work for a business unit spanning growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, experiments, and analyses; surface insights; communicate recommendations; and influence product, growth, finance, and operations leaders to drive measurable outcomes.

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 strong SQL or Python skills, independent analytical problem-solving, clear communication, and ownership of ambiguous, high-impact initiatives.

Develop predictive and statistical models, analytical datasets, data products, and AI-enabled workflows across marketing, ticketing, revenue, digital, and customer analytics. Analyze complex datasets, identify trends and data quality issues, validate AI-generated outputs, and translate business questions into scalable analytical solutions. Communicate technical findings and recommendations to technical and non-technical stakeholders.

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.

Own revenue-focused data science from opportunity sizing through production ML. Investigate and measure product changes, build and operate batch and online ML systems for personalization, experimentation, and automation, and partner cross-functionally to integrate models into user-facing products. Establish monitoring, lineage, reproducibility, and security practices; improve data pipelines; mentor data scientists; and define ML development best practices while using AI tools to accelerate delivery.

Own growth marketing data science for a key domain, developing optimization algorithms, attribution and incrementality models, experimentation strategies, analytics pipelines, dashboards, and strategic insights. Partner with marketing, product, engineering, data engineers, and ad platforms to improve customer acquisition, campaign performance, and marketing ROI. Serve as the end-to-end data domain owner and translate complex analyses into business decisions.

Staff Data Scientist responsible for owning ambiguous, high-impact product and business problems; designing experiments; analyzing product data; developing analytical tools and frameworks; partnering with product, engineering, design, and leadership; and mentoring data scientists. The role requires advanced statistical expertise, strong product judgment, production programming experience, and the ability to translate complex analyses into strategic recommendations.

Develop and optimize machine learning and statistical models for fraud detection and anti-money laundering. Analyze large datasets to identify fraudulent patterns, build scalable data pipelines and features, evaluate model performance, and collaborate with engineering, product, and operations teams. Communicate insights to technical and non-technical stakeholders while maintaining data privacy and security compliance. The role offers significant ownership in building Oscilar’s early machine learning stack within a remote-first startup.

Build and own marketing measurement, attribution, experimentation, and analytics for performance, influencer, and lifecycle marketing. Responsibilities include creating dashboards, debugging attribution across advertising platforms and CRM systems, designing incrementality and A/B tests, analyzing CAC, ROAS, payback, cohorts, LTV, and channel efficiency, developing attribution taxonomies and instrumentation, and translating findings into recommendations for marketing, product, finance, and executive stakeholders.

Leads end-to-end machine learning delivery for insurance transformation programs, including feature engineering, model development, validation, tuning, explainability, governance, and production readiness. Translates business objectives into analytics use cases and delivery plans, manages client relationships, coordinates distributed teams, and supports architecture, UAT, MLOps, monitoring, and deployment discussions. Requires strong Python, statistical modeling, SQL, large-scale data, stakeholder communication, and production ML experience.

Analyze microbiome and longitudinal clinical datasets, develop microbiome metrics and biomarkers using statistics and machine learning, create visualizations and reports, draft clinical research documents, support product and study development, and contribute to scientific publications and proposals.