Data Scientist

• Lead end-to-end performance diagnostics across customer, product, and advisor dimensions
• Translate curated data into actionable insights through hypothesis development, testing, analysis, and stakeholder storytelling
• Design and deliver scalable analytics assets, including datasets, dashboards, segmentation frameworks, and predictive AI/ML models
• Investigate, evaluate, and implement AI/ML tools and algorithms for complex business problems
• Develop visualizations and data stories for technical and non-technical audiences
• Partner with business owners to drive advanced analytics and AI/ML adoption
• Lead collaboration with data scientists, engineers, IT partners, and business process owners
• Provide subject-matter expertise, mentorship, and guidance on advanced analytics and AI/ML methodologies
• Identify emerging analytical trends and data needs to improve repeatable and scalable solutions

Full Stack Data Science Engineer building AI/ML analytics for TD, a major North American bank

Full Stack Data Science Engineer

Full Stack Data Science Engineer building scalable AI/ML models, dashboards, and data solutions. Supporting analytics-driven decisions at TD, a leading North American financial institution.

• Partner with business stakeholders, engineers, and subject-matter experts to define complex problems, requirements, and success criteria
• Assess data and technical feasibility, dependencies, risks, integration needs, and opportunities
• Plan and lead analytical initiatives from discovery and proof of concept through MVP, production deployment, and ongoing enhancement
• Design, develop, validate, and operationalize statistical, machine learning, simulation, and optimization solutions
• Apply software engineering, MLOps, and Responsible AI practices
• Provide technical direction, coordinate contributors, review deliverables, and remain involved in hands-on development
• Communicate analytical findings, assumptions, limitations, and evidence-based recommendations
• Collaborate with stakeholders, engineers, subject-matter experts, and multidisciplinary technical teams across iterative delivery stages

Data Scientist/ML Engineer developing production-ready analytics for Enbridge’s energy infrastructure operations

Data Scientist / ML Engineer

Data Scientist/ML Engineer developing production-ready machine learning and analytical solutions for Enbridge’s energy infrastructure operations. Leading initiatives from discovery through deployment and continuous improvement.

• Participate in TD's 18-month rotational Associate Program with two nine-month rotations
• Gain exposure to business areas within the Analytics Insights & AI team
• Perform data queries and prepare datasets for analysis and modeling, including data cleaning and feature engineering
• Analyze large datasets to identify trends and insights that inform business decisions
• Communicate findings to technical and non-technical audiences
• Collaborate with team members and business partners to develop, test, and deploy analytics solutions such as forecasting, optimization, and pricing models
• Identify business problems and opportunities appropriate for data-driven solutions
• Develop proposals and approaches for analytics initiatives
• Support business case development for analytics solutions
• Work on initiatives designed to improve decisions, processes, systems, and applications supporting TD's businesses

Graduate data science associate developing analytics solutions for TD, a major North American bank

Data Science Associate – Graduate

Graduate data science associate rotating across TD’s Analytics Insights & AI team. Building predictive, optimization, and pricing models for a leading North American bank.

• Gain proficiency in and perform exploratory data analysis and present insights
• Prepare datasets for visualization and machine learning modeling
• Train machine learning systems using data annotation and benchmarking techniques
• Assess AI/ML model performance and propose improvements with colleagues
• Prepare data visualizations using industry-leading tools for stakeholders
• Document results, successes, and lessons learned
• Develop a capstone project proposal contingent on successful early work
• Engage with relevant people to inform the capstone work
• Build a prototype or visualization
• Assess and present capstone results
• Work under the direct supervision of the Manager, Data Science

Data Science Student developing analytics and machine-learning solutions for PCL Construction

Data Science Student

Data Science Student supporting PCL Construction’s infrastructure projects through exploratory analysis, machine learning, and visualizations. Building a portfolio-focused capstone prototype under data science supervision.

• Participate in developing fraud detection, anti-money laundering, and claim automation products
• Work with structured data, free text, documents, and images
• Implement data engineering from client extracts to insertion into SQL and ElasticSearch data stores
• Develop, test, tune, and productionize models for fraud detection and automation detection in complex environments
• Automate key business tasks in the production process framework using C#
• Conduct client meetings and interact with external stakeholders
• Gather user feedback, present business cases, and define product evolution roadmaps

Applied Data Scientist/Engineer building AI insurance solutions for Shift Technology

Applied Data Scientist/Engineer

Applied Data Scientist/Engineer developing data pipelines and machine-learning models for Shift Technology’s insurance AI platform. Client-facing work across fraud detection, anti-money laundering, and claims automation.

• Partner with Client Success and Marketplace Teams as the data subject-matter expert for Revenue segments and merchants
• Create business cases, insights, dashboards, and experiments for Affirm’s external merchant base
• Create internal analyses to assess Revenue performance, drive recommendations, and inform segment strategy and execution
• Standardize common use cases and provide analytics infrastructure
• Automate analyses and dashboards to scale decision-making within Revenue and with external merchants
• Define KPIs, performance benchmarks, and methodologies
• Identify and work with structured and unstructured in-house and third-party data sources
• Provide project and analytics guidance to cross-functional teams as the analytics point of contact
• Propose, monitor, and contextualize experiment design and execution to optimize go-to-market efforts, quantify Affirm’s value to prospective merchants, and support upselling

Revenue analytics lead scaling merchant insights for Affirm’s buy-now-pay-later platform

Analytics Lead, Full Stack – Strategic Revenue Insights

Revenue analytics lead building dashboards, experiments, and data solutions for Affirm’s buy-now-pay-later platform. Partnering with merchants and internal teams to optimize growth decisions.

• Participate in TD’s 18-month AI2 Associate rotational program across Analytics Insights & AI
• Perform data queries and prepare datasets for analysis and modelling
• Clean data and conduct feature engineering
• Analyze large datasets to identify trends and insights for business decisions
• Communicate findings to technical and non-technical audiences
• Support data-driven solutions by building models using structured and unstructured data
• Prepare datasets for machine learning through integration, cleaning, feature engineering, scaling, and transformation
• Collaborate with team members and business partners to develop, test, and deploy analytics solutions such as forecasting, optimization, and pricing models
• Identify business problems and opportunities suitable for data-driven solutions
• Develop proposals and approaches for analytics initiatives
• Support business case development for analytics solutions
• Work on initiatives that improve decisions, processes, systems, and applications supporting TD’s businesses
• Participate in programming, coaching, tools, and support provided through the Associate program

Data Science Associate building machine-learning models for TD, a major North American bank

Data Science Associate

Data Science Associate rotating through TD’s Analytics Insights & AI team. Building machine-learning and analytics solutions for one of North America’s leading banks.

• Design models and strategies to quickly detect transactional anomalies
• Explore, analyze and synthesize information to understand the characteristics of active fraud schemes
• Develop strategies for detecting transactional fraud
• Test the theoretical performance of fraud mitigation models, rules and tools
• Deploy fraud mitigation models, rules and tools into production
• Continuously monitor fraud patterns and model and strategy performance
• Research and develop predictive variables to improve fraud detection
• Develop, update and produce dashboards, data visualizations and reports
• Optimize and automate key fraud detection analytics processes
• Provide ad hoc advice on projects to improve fraud risk management tools
• Advise risk management teams and business sectors in the field
• Monitor industry trends to develop and update organizational best practices

Data Scientist detecting transactional fraud for Desjardins cooperative financial group

Data Scientist, Fraud Detection

Data Scientist detecting transactional fraud for Desjardins, North America’s largest cooperative financial group. Building, deploying, and monitoring analytics models, rules, dashboards, and mitigation strategies.

• Mine, evaluate, feature engineer, inspect and adjust available data, and select predictors for optimal use in predictive modeling
• Analyze client and partner needs, anticipate the potential impacts of built models and their operationalization, and help roll the models out
• Use statistical, machine learning and artificial intelligence methods to use data, and develop and maintain models and segmentations that meet business needs
• Create the programming needed to prepare and mine data, and develop, evaluate, support, roll out and manage complex models
• Prepare easy-to-understand presentations and data visualizations supporting business decisions and present to committees
• Specify the needs and constraints for ensuring built model performance and keep documentation on data and calculation rules up to date
• Identify opportunities to improve rules and systems, and recommend enhancements for data management tools
• Provide data and guidance for business decisions, advanced analytics and artificial intelligence mandates
• Serve as a data use resource person for stakeholders and present analysis results

Data Scientist developing predictive models and analytics for Desjardins, a cooperative financial group

Data Scientist, Data Valorization

Data Scientist developing predictive models and advanced analytics for Desjardins, North America's largest cooperative financial group. Supporting business decisions through data mining, AI, and stakeholder guidance.

• Identify and qualify, with stakeholders, high-potential use cases for AI, advanced analytics, and automation within Finance
• Assess technical feasibility, business value, and adoption potential to prioritize initiatives
• Design and develop proofs of concept, pilots, and MVPs within a few weeks to validate or reject hypotheses
• Develop models, algorithms, and AI agents for Finance teams
• Explore, test, and compare new AI technologies to inform technology choices
• Analyze financial and operational data to identify trends, risks, and optimization opportunities
• Support Finance teams in experimenting with and adopting solutions and act as an AI resource
• Ensure responsible, secure, and compliant use of data and AI solutions
• Prepare validated solutions for production with relevant technology teams

Senior Data Scientist building AI and analytics solutions for Desjardins’ Finance function

Senior Data Scientist, Finance

Senior Data Scientist developing AI, advanced analytics, and automation solutions for Desjardins’ Finance teams. Building proofs of concept, models, and AI agents for production adoption.

• Lead and grow the Data Governance function, setting strategic direction for data ownership, stewardship policy, access governance, and data classification standards
• Own and operate DataHub as the living catalog of the organization’s data assets, including metadata management, catalog onboarding, tagging, lineage registration, and domain stewardship
• Evaluate and deploy complementary governance, observability, privacy, entitlement, and quality platforms
• Own the enterprise Data Quality Framework covering Completeness, Accuracy, Timeliness, Consistency, Validity, and Uniqueness
• Classify data assets into quality tiers and enforce tier-specific SLAs and standards
• Document, publish, and review data quality SLAs by domain and tier
• Define and publish data health score methodology and report domain scores monthly to the data domain council
• Conduct quarterly data quality audits, share findings, and assign remediation with accountability and due dates
• Define and maintain dataset profiling policies and escalation thresholds
• Govern the separation between defining quality rules and implementing them; escalate breaches and track resolution
• Own access control and entitlements governance
• Lead compliance alignment across privacy regulations, internal audit requirements, and industry standards
• Partner with Legal and Security on current data handling practices
• Facilitate the cross-functional data domain council
• Build a culture of data accountability through training, communication, and stakeholder engagement

Manager governing data quality, access, and metadata for Benevity’s social-impact technology platform

Manager, Data Governance

Manager leading data governance, quality, access, and metadata at Benevity, whose technology enables corporate social action. Building standards, catalogs, audits, and accountability across the organization.

• Lead and grow the Data Governance function and set strategic direction for ownership, stewardship, access governance, and classification standards
• Own and operate DataHub as the living catalog of organizational data assets
• Evaluate and deploy complementary governance, observability, privacy, entitlement, and quality platforms
• Own the enterprise Data Quality Framework across six quality dimensions
• Classify data assets into quality tiers and enforce tier-based SLAs and standards
• Document, publish, and review data quality SLAs
• Define and publish data health score methodology and report domain scores monthly
• Conduct quarterly data quality audits, assign remediation, and track accountability and due dates
• Define and maintain dataset profiling policies and escalation thresholds
• Govern the separation between quality rule definition and implementation by Engineering and DataOps
• Own access control and entitlements governance
• Lead alignment with privacy regulations, internal audit requirements, and industry standards
• Partner with Legal and Security on current data handling practices
• Facilitate the cross-functional data domain council
• Build organizational data accountability through training, communication, and stakeholder engagement

Data governance manager advancing Benevity’s social-impact technology platform

Data Governance Manager strengthening Benevity’s social-impact technology platform. Defining data standards, quality frameworks, catalog practices, and access governance across the organization.

• Act as the program management layer for the Data team during audits, interfacing with internal Audit coordination teams, Risk and Security, Finance, Compliance, and external auditors
• Own audit readiness as a continuous, always-on practice
• Create and manage inventories of audit tests, expected requests, and response playbooks for Data team-owned systems and processes
• Maintain a catalog of audit-scoped data sources, including known issues, readiness status, and improvement areas
• Support ongoing testing, including mock testing and identification of areas requiring reengineering
• Align with Finance, Security, and Compliance stakeholders to provide guidance and coordination during audit activities
• Implement tagging and governance processes with Data Governance to identify endorsed datasets and tables considered audit ready
• Manage the auditor data access role to ensure access is fit for purpose
• Build inventories, playbooks, catalogs, and governance processes that translate audit requests into coordinated, well-documented, repeatable responses

Technical Program Manager driving audit readiness for Kraken’s global crypto and open-finance infrastructure

Senior Technical Program Manager, Data & Audit Readiness

Technical Program Manager driving audit readiness for Kraken’s global crypto and open-finance infrastructure. Coordinating audits, governance, inventories, playbooks, and auditor data access for Payward’s data platform.

• Evolve Attribution Modelling & Methodology
• Manage and improve our first-touch attribution model, incorporating new signals and partnering with Analytics to align on business definitions, methodology, and interpretation.
• Enhance our multi-touch attribution model, including bringing in non-clickable sources such as connected TV and Salesforce data, in partnership with Analytics on methodology and interpretation.
• Build model comparison analyses and reporting to evaluate newly developed models against existing first-touch and multi-touch approaches.
• Act as the primary point of contact for diagnosing attribution inconsistencies, partnering with Marketing and Customer Analytics stakeholders to understand perceived under-performance and uncover root causes in data and configuration.
• Integrate new marketing attribution sources end-to-end, including partnering with our data teams to develop reverse ETL models and seed tables needed to sync data into the attribution systems
• Maintain and update attribution seed tables (e.g., adding influencers/partners and implementing UTM mapping changes). Partner with BI to review published PRs and confirm changes meet standards for accuracy, integrity, and alignment with attribution logic.
• Monitor and validate UTM structures and attribution signals, proactively identifying mapping issues and irregularities that impact reporting accuracy.
• Coordinate with Ad Operations to ensure tracking configurations align with attribution needs and that collected web data is structured and usable for modelling.
• Write and maintain production-quality SQL to analyze raw marketing, product, and revenue signals into analysis-ready and reverse ETL datasets.
• Translate data analysis into dimensional/semantic model requirements (e.g., clean attribution-ready tables, standardized channel mappings, and reusable metrics definitions) that downstream reporting and stakeholders can trust.
• Collaborate with the relevant data teams to review and maintain data models that support first-touch and multi-touch attribution, including new source onboarding, table improvements, and model comparison reporting.
• Review and maintain quality checks and validation logic to ensure changes to data models, UTMs, and attribution logic don’t break downstream reporting.
• Partner with BI and stakeholders to align on definitions, logic, and documentation, ensuring datasets are understood, governed, and reusable across the org
• Lead attribution onboarding/training for new Marketing hires, including how account-level attribution is derived and interpreted
• Develop and maintain documentation for:
• Attribution logic and frameworks
• Seed table dictionaries
• UTM standards
• Model configuration
• Support proactive enhancements that improve attribution ROI and stakeholder confidence.
• Help operationalize prospect data flows (e.g., cleaning/loading cold prospect data into tools used for retargeting, sequencing, and warming), so GTM teams can execute reliably

Marketing Data Manager driving attribution excellence for Jobber's Demand Generation team

Marketing Data Manager

Marketing Data Manager driving attribution excellence for Jobber's Demand Generation team. Managing marketing attribution and data enablement while collaborating with various teams for optimal performance.

• Assist in preparing, transforming, and deeply analyzing complex data from real-world sources.
• Contribute to building, validating, and maintaining customer-facing machine learning models and robust data pipelines.
• Help break down project requirements into small, actionable, and deliverable slices.
• Write clean, well-documented code while following software engineering best practices.
• Champion efforts to improve data quality and integrity across the organization.
• Deep-dive into datasets to identify hidden patterns and uncover fresh opportunities.

Data Scientist at Miovision for smart transportation analytics.

Data Scientist I

Data Scientist at Miovision applying statistical modeling and AI for smart transportation solutions. Analyzing global transportation data to drive insights and solutions for infrastructure challenges.

• Build the measurement architecture, behavioral models, and causal infrastructure.
• Develop automated analytics workflows with defined guardrails.
• Push back on attribution and determine what is worth measuring.
• Design AI analytics systems with rigor and evaluate generated insights.

Staff Data Scientist shaping client understanding for a fintech company

Staff Data Scientist

Staff Data Scientist at Purpose Unlimited building client understanding and measurement architecture. Shaping behavioral models and analytics for the wealth platform in a fintech environment.

• Utilize machine learning and advanced statistical methods to identify trends and patterns in complex datasets.
• Convert complex databases into actionable insights and recommendations that can drive business strategy.
• Keep up with the latest trends and methodologies in data science and incorporate them into your work.
• Assist in maintaining and improving our data mining tools and platforms to ensure their efficiency and reliability.
• Make strategic recommendations based on data analysis to support decision-making processes.
• Work closely with other departments to promote the adoption of data-driven decision-making across the organization.
• Validate the quality of analytical approaches and outputs produced by other team members to ensure high standards.

Data Scientist converting complex data into actionable insights and solutions at Intact.

Data Scientist II

Data Scientist at Intact transforming complex data into actionable insights and strategies. Engaging in multidisciplinary teamwork in a modern data ecosystem.

• Coordinate all Data Capture projects from project handoff through completion.
• Translate signed client contracts into executable field deployment plans.
• Build detailed project timelines and execution schedules.
• Ensure project milestones remain on track.
• Identify risks early and proactively resolve scheduling conflicts.
• Coordinate multiple concurrent deployments across North America and internationally.
• Become the primary operational contact for clients before and during field deployments.
• Clarify project scope
• Confirm site access requirements
• Coordinate scheduling
• Manage logistics
• Confirm deliverables
• Communicate schedule changes
• Ensure clients always know what happens next.
• Partner closely with Sales, Project Management, Data Capture Team, Geospatial Team, Product & Engineering Operations, Finance, Logistics Management.
• Coordinate everything required for successful field deployments, including: Equipment shipments, Drone logistics, Site access documentation, Safety documentation, Field schedules, Resource planning.
• Work closely with the Director and Data Capture Field Lead to: Schedule technicians, Balance workloads, Reduce unnecessary travel, Improve utilization, Forecast upcoming resource needs.
• Ensure information flows smoothly between every stakeholder.

Project Manager for Data Capture Operations at Niricson using advanced technology.

Data Capture Operations – Project Manager

Project Manager overseeing Data Capture Operations at Niricson, utilizing advanced technology for infrastructure inspection. Coordinating projects, ensuring smooth deployments, and fostering team collaboration.

• Manage data and reporting projects that power strategic decision-making across the Fibre build and Go-To-Market (GTM) initiatives nationally.
• Be the architect who brings fragmented data together.
• Connect the dots across disparate data sources to create unified views in GCP and other platforms.
• Write SQL queries, develop data pipelines, create automated reporting solutions, and build integrations.
• Design and build compelling dashboards in Looker Studio Pro and Tableau.
• Partner with stakeholders and developers to articulate technical requirements and review solutions.
• Lead data projects from scoping to delivery, managing timelines, stakeholders, and technical requirements while ensuring data quality and accuracy.

Senior Project Manager driving high complexity data & reporting initiatives at TELUS

Senior Project Manager – Data & Reporting

Senior Project Manager managing data and reporting for TELUS's national Fibre build. Collaborating with teams to deliver actionable insights driving strategic decision-making.

• Design and build robust data pipelines and architectures on Google Cloud Platform (GCP), leveraging BigQuery, Workflows, Cloud Scheduler, Dataproc, and Batch jobs
• Establish scalable ETL/ELT processes that efficiently ingest, transform, and prepare data from diverse sources for analytics and reporting
• Build and manage GCP resources using Pulumi and YAML configurations, and maintain code in GitHub
• Conduct in-depth exploratory data analysis and apply statistical methods, machine learning techniques, and AI-driven analytics to uncover patterns, trends, and actionable business insights
• Develop analytical models and leverage AI capabilities to support strategic decision-making and operational improvements across key business metrics
• Design and build intuitive, visually compelling dashboards and reports in Looker Studio that communicate complex data stories to diverse stakeholders
• Create data visualizations with a strong eye for clarity, aesthetics, and user experience that enable self-service analytics
• Leverage GCP's data ecosystem (BigQuery, Workflows, Cloud Scheduler, Dataproc, Batch, and Vertex AI) to optimize query performance, reduce costs, and enable real-time analytics
• Implement best practices for data governance, security, and scalability within GCP environments
• Work with business teams and stakeholders to understand data requirements and translate business questions into analytical solutions
• Document analytical findings and architectural decisions clearly, and mentor team members on data best practices.

Data Scientist creating data-driven solutions to optimize TELUS's operations

Data Scientist developing scalable data architectures and analytics solutions for TELUS's operations. Collaborating with stakeholders and stakeholders to drive business decisions through data insights.

Data Scientist jobs in Canada

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Full Stack Data Science Engineer

Data Scientist / ML Engineer

Data Science Associate – Graduate

Data Science Student

Applied Data Scientist/Engineer

Analytics Lead, Full Stack – Strategic Revenue Insights

Data Science Associate

Data Scientist, Fraud Detection

Data Scientist, Data Valorization

Senior Data Scientist, Finance

Manager, Data Governance

Manager, Data Governance

Senior Technical Program Manager, Data & Audit Readiness

Marketing Data Manager

Data Scientist I

Staff Data Scientist

Data Scientist II

Data Capture Operations – Project Manager

Senior Project Manager – Data & Reporting

Data Scientist