Top Data Science Jobs in Montreal

Owns credit policy development and governance for Square Financial Services, including underwriting strategy, policy experimentation, portfolio monitoring, regulatory documentation, and risk reporting. Designs rigorous champion-challenger tests, randomized holdouts, and staged rollouts while balancing growth, losses, profitability, fair lending, and bank oversight requirements. Partners with Risk, Compliance, Internal Audit, and senior leadership to scale credit products responsibly.

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 deploy AI, machine learning, and NLP solutions for compliance surveillance and non-financial risk. Design LLM, RAG, agentic AI, and knowledge graph systems; automate manual processes; research advanced analytical methods; evaluate models; and collaborate with Compliance, Legal, Financial Crimes, and IT stakeholders to detect suspicious and illegal activity.

Develop, validate, monitor, and maintain credit risk models supporting risk quantification and credit decisions. Lead modeling projects, advise practitioners, document mathematical models, interpret member and client credit behavior, and ensure alignment with business needs, regulatory standards, Basel and IFRS requirements. Coach team members, communicate complex information clearly, and monitor industry trends to improve modeling practices.

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.

Build and deploy large-scale LLM-based recommender systems from raw customer and catalog data through production inference. Responsibilities include feature and context engineering, distributed training on SageMaker HyperPod, LoRA/QLoRA fine-tuning, reinforcement learning, deep learning architecture, ranking, inference optimization, evaluation, privacy protection, and stakeholder communication with Expedia and AWS science teams.

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

Own end-to-end data science for a business area such as growth, product, AI, or strategic partnerships. Define business questions, metrics, reports, analyses, and experiments; generate actionable insights; and influence product, growth, finance, and operations decisions. The role requires independently structuring ambiguous problems, using SQL or Python, communicating findings clearly, and driving measurable business outcomes.

Own end-to-end data science for growth, product, 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 strong SQL or Python skills, independent analytical problem-solving, clear communication, and ownership of ambiguous 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 measurable outcomes with cross-functional stakeholders. The role requires strong analytical thinking, independent data work using SQL or Python, ownership of ambiguous problems, and persuasive communication with leadership.

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.

Develop and implement credit risk decision strategies, predictive models, and machine learning solutions for lending products. Lead strategic risk transformation initiatives, validate and document methodologies, support governance and regulatory processes, advise senior stakeholders, and mentor analytical team members.

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.

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.