Top Data Science Jobs in Montreal

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.

As a Data Scientist at Mistplay, you'll design and optimize the rewards economy, implement machine learning systems, and analyze strategies to enhance user engagement and business outcomes.

Lead data-driven strategy by partnering with senior executives to deliver rigorous analytical solutions. Design and implement sophisticated analyses on large-scale data, build trusted cross-functional relationships, mentor data scientists, and influence long-term product and business decisions.

Embedded on a core business team, define the right problems, build metrics and reports, run analyses and experiments, surface actionable insights, and drive data-informed decisions end-to-end across growth, product, AI, or partnerships.

Embed within a core business area to define problems, build metrics, run analyses and experiments, surface insights, and drive data-informed decisions end-to-end across growth, product, AI/automation, or partnerships.

Embedded data scientist owning analytics for a business unit: define problems, build metrics/reports/experiments, surface insights, and drive data-informed decisions across growth, product, AI, and partnerships.

Embed with a core business area to define problems, build metrics/reports, run analyses and experiments, and drive data-informed decisions end-to-end. Translate ambiguous questions into clear hypotheses, surface insights, and partner with product, growth, finance, and ops to execute recommendations and deliver measurable business impact.

Embedded on a central Data Science team, you will define problems, build metrics/reports, run analyses and experiments, surface insights, and drive decisions end-to-end for growth, product, AI/automation, or partnerships.

Embedded on a business unit, define analytic questions, build metrics/reports, run analyses and experiments, surface insights, and drive data-driven product, growth, AI, or partnership decisions end-to-end.

The Junior Data Scientist will design and develop data models using Machine Learning and Analytics for trading and compliance in finance.

Lead and execute statistical and geospatial analyses to quantify and map climate mitigation potential from forests and peatlands. Design analytical workflows, manage projects and partners, create datasets, tools, reports, and deliver trainings and technical support for global conservation initiatives. Communicate results via publications, visualizations, and capacity-building activities; occasional fieldwork and international travel possible.

Founding Staff Data Scientist to build decision science from the ground up: design and deliver forecasting, optimization, experimentation, personalization, and recommendation systems; establish modeling standards, validation and deployment playbooks; mentor data scientists and integrate reusable decision workflows with the ML platform.

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.

Design and analyze experiments and causal studies, build retention measurement and predictive models, develop scalable PySpark/SQL data pipelines, create dashboards, and partner with product and growth teams to improve user engagement and retention.

The Data Scientist supports customer use of the Epistemix platform through data integration, modeling, and visualization, improving decision-making and customer satisfaction.

Proactively explore RevenueCat data to identify customer problems and opportunities. Translate ambiguous product questions into analyses and production-grade predictive/descriptive models. Partner with Product, Engineering, and Analytics to shape roadmaps, define experimentation and benchmarking approaches, deploy and iterate on models powering customer-facing features, and communicate insights across technical and non-technical audiences.

The Data Scientist will develop data labeling strategies, optimize datasets for LLMs, analyze data quality, and collaborate with engineering on data processing pipelines.

Use diverse data sources to evaluate product performance, build automated ETL/data workflows, design dashboards and KPIs, own data product quality, and provide analytical insights to guide product and business decisions while establishing monitoring frameworks and best practices.

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.

The Staff Data Scientist will own the analytical strategy for Firefox, driving data-informed decisions through measurement frameworks, experiments, and user growth analysis, while mentoring other data scientists.

As a founding Data Scientist/Machine Learning Engineer, you'll develop AI/ML models, enhance product capability, and drive impactful user outcomes while working closely with product teams.

Lead the Growth pod as the first dedicated data hire: build data infrastructure connecting event, product, GTM, and financial data; define metrics and run experiments with proper power analysis; build predictive models and reporting/automation; proactively surface insights and embed with Product, Engineering, and Design to drive activation, expansion, and retention. Bring applied AI to improve growth outcomes.

Lead a team of Data Scientists and Data Engineers to create data assets and insights, impacting game development and business strategy through analytics.

Lead development and vision for the Client Journey Database (CJDB), make data products AI-ready, collaborate with stakeholders, build user-friendly tools, transform complex data into actionable insights, validate analytic approaches, promote data governance and a data-centric culture, and tackle data engineering and IA engineering challenges.

Founding Data Scientist for RC Capital responsible for building underwriting, fraud detection, anomaly detection, and lead-qualification models; integrating risk signals into product flows; deploying and monitoring production models; analyzing cross-platform revenue data; and defining DS strategy to scale fintech products.