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.

The Data Scientist will partner with Sales, Revenue Operations, and Finance to analyze GTM performance, build and productionize machine learning models, conduct experiments, forecast outcomes, and identify growth opportunities. Responsibilities include statistical analysis, causal inference, Python and SQL development, MLOps, agentic workflow automation, semantic-layer contributions, executive reporting, and communicating insights to nontechnical stakeholders in a fully remote environment.

Develop and optimize credit risk strategies for personal financing products across their lifecycle. Apply machine learning, predictive modeling, SAS, Python, and SQL to analyze credit data, monitor strategy performance, and recommend improvements. Build quarterly Power BI reports, conduct ad hoc analyses, document decision-making strategies, and advise stakeholders on credit risk management.

Develops end-to-end statistical, machine learning, generative AI, and automation solutions for underwriting and claims. Responsibilities include analyzing structured and unstructured data, preparing and deploying models, evaluating results, monitoring production performance and model drift, documenting solutions, managing privacy and model risk, and collaborating with business, IT, project, and data teams. The role also supports responsible AI adoption and translates complex concepts for varied audiences.

Develops AI, data science, advanced analytics, and automation solutions for Finance. Responsibilities include identifying use cases, building prototypes and MVPs, experimenting with generative AI, LLMs, AI agents and RAG, integrating APIs and data sources, analyzing financial data, advising stakeholders, and preparing validated solutions for production. The role requires translating ambiguous business problems into measurable experiments and communicating technical concepts to nontechnical audiences.

Designs and deploys production-grade data science and AI solutions across UKG products. Responsibilities include advanced statistical and machine learning modeling, generative AI and RAG development, end-to-end ML pipelines, model lifecycle management, evaluation frameworks, production engineering, and cross-functional collaboration. The role also involves mentoring junior data scientists, leading projects, communicating insights, and contributing to AI roadmap execution.

Lead end-to-end data science and AI initiatives using statistical methods, machine learning, generative AI, and big data. Analyze billions of records, develop production-quality prototypes, and collaborate with engineering teams to deploy scalable solutions using cloud technologies such as Vertex AI. Provide technical leadership, mentor team members, communicate findings to technical and nontechnical stakeholders, and establish data science best practices.

Analyze complex business problems using statistics, programming, and machine learning. Responsibilities include preparing datasets, developing predictive and statistical models, performing segmentation, regression, forecasting, and optimization, and evaluating model performance. The role requires Python or R, SQL, Pandas, NumPy, and notebooks, with collaboration on productionizing analytical solutions and communicating results to stakeholders. French fluency is required; experience with cloud data platforms, visualization tools, and model deployment is advantageous.

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

Own end-to-end data science for growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; generate insights; and drive recommendations and decisions with cross-functional stakeholders. The role requires strong SQL or Python skills, independent analytical problem-solving, ownership of ambiguous initiatives, and persuasive communication with leadership.

Own end-to-end analytical work for a business unit across growth marketing, product, AI and automation, or strategic partnerships. Define business questions and metrics, build reports, conduct analyses, design experiments, and communicate insights and recommendations to stakeholders. The role requires independently structuring ambiguous problems, using SQL or Python, influencing leadership decisions, and driving measurable business outcomes.

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

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.

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.

The Senior Data Scientist will develop and deploy full-stack machine learning, data engineering, and AI systems; perform advanced statistical modeling; apply NLP and generative AI to unstructured data; optimize large-scale databases and computing workflows; build interactive analytics tools; and mentor teams while supporting client engagements.

Own analytical relationships with HTS partners, delivering scalable reporting on inventory, pricing, and performance. Conduct exploratory analysis, apply machine learning and statistical methods, build dashboards, automate reporting with AI tools, and translate complex findings into clear recommendations. Collaborate with product and engineering teams to improve data collection and support partner decision-making.

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.

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.

Lead the development and deployment of production-grade machine learning models for customer lifetime value, lead scoring, recommendations, ranking, personalization, and marketing optimization. Build scalable ML workflows using Databricks and MLflow, establish model monitoring and reproducibility standards, evaluate business impact, and mentor data science practitioners while collaborating with Engineering, Product, Marketing, and Analytics.

Lead analytics and data science initiatives that optimize Coalition’s cyber insurance business. Responsibilities include experimentation, statistical analysis, funnel metric development, dashboarding, data modeling, scalable analytical workflows, executive reporting, roadmap recommendations, and mentoring junior analysts and scientists. The role partners with product, engineering, insurance, and revenue stakeholders to identify trends, improve customer experience, and support safe revenue growth.

Develop machine learning models and detection rules for web security products. Analyze large datasets to distinguish legitimate from fraudulent traffic, improve detection accuracy, support production data workflows, troubleshoot distributed systems, and provide insights that guide product improvements and strategy.

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 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.