Top Data Science Jobs in Montreal

Leads data science methodology and advanced analytics for AI-enabled Medical Affairs workflows. Develops and evaluates statistical, machine-learning, NLP, and AI models using healthcare and real-world data; defines validation, monitoring, and decision frameworks; partners with engineering, governance, and business teams to operationalize solutions. Provides technical leadership, mentorship, documentation standards, and strategic guidance while ensuring privacy, compliance, responsible AI use, and clear communication of analytical findings.

Senior Data Scientist responsible for transforming large tabular and user-behavior datasets into reliable machine learning inputs. Coordinates with ML, Product, engineering, TPM, and annotation teams; defines data and training requirements; oversees dataset curation and processing; translates project goals into actionable specifications; and ensures data integrity, accuracy, representativeness, and production readiness.

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.

Develop and optimize machine learning and statistical models for fraud detection, authentication, identity verification, and related risk use cases. Responsibilities include data discovery, feature engineering, experimentation, model evaluation, monitoring, documentation, governance support, and productionization. The role requires analyzing customer and fraud behavior, assessing business impact, presenting findings, and collaborating with Strategy, Engineering, Product, Data, Model Validation, and Risk teams.

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.

Lead end-to-end AI and data science initiatives across Alcoa’s Operations and Enterprise functions. Develop, validate, deploy, and continuously improve statistical, machine learning, generative AI, RAG, and agentic AI solutions. Translate business challenges into scalable models, evaluate outcomes, and deliver measurable value. Provide technical leadership, mentor data scientists, and collaborate with engineers, architects, analysts, and stakeholders to build trusted, responsible AI capabilities.

Evaluate AI model outputs on data science, statistics, machine learning, experimentation, and quantitative problems. Develop expert prompts, datasets, and reference solutions; identify statistical and methodological errors; and provide structured feedback to improve model performance. The role requires Python, machine learning, statistics, data analysis, experimentation, Pandas, and data modeling skills.

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.

Explore large-scale AI evaluation datasets, identify patterns, biases, and causal relationships, and design experiments to understand model behavior. Build reproducible analysis pipelines with Python, Pandas, NumPy, and Spark; develop statistical and causal inference frameworks; partner with ML researchers and engineers; and communicate findings to technical and non-technical stakeholders.

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.

Design and execute product analyses using raw user behavior data, own A/B experiments from design through interpretation, and translate findings into recommendations for product and engineering teams. Build repeatable analytical tooling, apply statistical modeling, and use AI tools to accelerate exploration, analysis, and insight delivery across consumer products, media, gaming, and e-commerce.

Own the analytical function 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 product, growth, finance, and operations decisions. The role requires independent data work, strong analytical and communication skills, first-principles problem solving, and ownership of ambiguous initiatives through measurable business impact.

Own the analytical function for a core business area such as growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; generate insights; communicate recommendations; and drive decisions and measurable outcomes. The role requires independent data work, first-principles thinking, stakeholder influence, and end-to-end ownership of ambiguous problems.

Own end-to-end data science for growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; generate actionable insights; and influence product, growth, finance, and operations decisions. The role requires independently structuring data, solving ambiguous problems, communicating findings persuasively, and driving measurable business outcomes.

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.

Own the analytical function for an embedded business unit across growth, product, AI and automation, or strategic partnerships. Define business questions, metrics, reports, analyses, and experiments; generate insights; communicate recommendations; and drive decisions and measurable outcomes. The role requires independent data work, strong analytical thinking, persuasive communication, and ownership of ambiguous problems.

Own end-to-end data science and analytics for a business area such as growth, product, AI and automation, or strategic partnerships. Define business questions and metrics, extract and structure data, conduct analyses and experiments, build reports, communicate insights, and drive data-informed decisions with cross-functional stakeholders. The role requires strong analytical thinking, independent problem ownership, and persuasive communication.

Own the analytical function 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 to leadership; and drive decisions and outcomes end to end. The role requires independent data work, first-principles problem solving, strong communication, and ownership of ambiguous, high-impact initiatives.

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.

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.