Top Data Science Jobs in Montreal

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 complex data science and advanced analytics initiatives to support business decisions. Oversees data mining, feature engineering, data quality, predictive modeling, segmentation, and model deployment. Develops programming for data preparation and model management, creates visualizations and executive presentations, establishes modeling guidelines, supports documentation and training, and improves data management tools. Acts as a subject matter expert and advisor while coordinating stakeholders and translating business needs into analytical solutions.

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.

Develops, monitors, and maintains credit risk models to support financing products and credit decisions. Designs mathematical risk models, prepares documentation, leads projects, advises stakeholders, explains complex information, and monitors industry trends. Requires expertise in credit risk quantification, Basel and IFRS standards, and SAS, SQL, and Python, along with French and English proficiency.

Own end-to-end data science for a core business area such as growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; generate actionable insights; and influence leadership decisions. The role requires independent analytical work, strong SQL or Python skills, first-principles problem solving, persuasive communication, and ownership of ambiguous problems. Candidates need 6+ years of experience in consulting, investment banking, or high-growth technology.

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 a core business area such as growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; generate actionable insights; communicate recommendations; and drive decisions and measurable 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 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.

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.

Lead data science initiatives for Risk and Support Operations by analyzing customer and product data, defining metrics, forecasting demand, evaluating experiments and risk policies, and building decision frameworks. Use AI tools and agent workflows to improve analytical efficiency. Partner with product, engineering, and risk teams, communicate insights to senior stakeholders, establish technical standards, mentor data scientists, and support hiring.

Analyze product, customer, risk, and support data to guide decisions across Block’s platforms. Own metrics, forecasting, staffing models, experiments, visualizations, and measurement frameworks. Evaluate risk models and policies for performance, bias, and drift; use AI tools and agent workflows to improve analytical execution. Partner with product, engineering, and risk stakeholders, lead technical standards, build reusable tooling, mentor data scientists, and contribute to hiring.

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.

As a Staff Data Scientist, you will analyze large-scale broadband data, develop and validate ML models, and integrate solutions into Calix Cloud, collaborating with teams for enhanced network intelligence and subscriber experience.

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.

The Data Scientist will design algorithms for insights, structure data, collaborate with machine learning engineers, and analyze datasets to improve workflow automation and product value.

Work on Alpha Science to source, clean, and build datasets; design data pipelines; perform statistical and ML analyses; build dashboards and reusable tools; communicate findings to deal teams; and leverage LLM-powered tools to support investment decision-making.

Lead high-complexity data science initiatives improving search ranking, retrieval, relevance, and evaluation. Develop and deploy production machine-learning and NLP solutions, investigate LLM-based ranking features, optimize SQL queries, and work with sparse privacy-preserving data. Define strategies for ambiguous problems, guide other data scientists, collaborate with product teams, and own projects from scoping through production.