Data Scientist

• Own experimentation (the heart of the role). Partner with Product and Commercial teams to design, run, and read growth experiments — from framing the hypothesis and sizing the test to calling the result and pushing the decision. Bring rigor (power analysis, causal inference, guardrail metrics) to a business moving fast.
• Build the reporting backbone. Own the core dashboards and self-serve tooling the segment runs on, evolving an AI-augmented reporting stack that already automates much of the routine work. Your job is to make performance easy to understand for execs.
• Generate insight, not just analysis. Break down revenue and margin performance into drivers - volume, mix, pricing, cohort behavior - and proactively tell us where growth is coming from, where it's leaking, and what to do next.
• Keep the data foundation healthy. Manage the partnership with our central Data Engineering team to make sure the segment's core data models and pipelines support the analytics.
• Sharpen the forecast. Support Strategic Finance with driver-based revenue forecasting analytics, connecting operational signals to financial outcomes.

Lead Data Scientist – Growth & Experimentation

• Lead Data management activities and ensure study deliverables meet timelines and quality standards.
• Review of Clinical research documents (e.g. protocols, Case Report Forms).
• Create and review electronic Case Report Form (eCRF) specifications.
• Develop critical DM documents such as the Data Management Plan, Data Validation Plan, eCRF Completion Guidelines, Data Entry Guidelines and Data Review Plan.
• Develop edit rules/checks, query logic and query messages.
• Develop User Acceptance Testing Plan and perform testing on data entry screens and edit checks.
• Create and provide training to the clinical team and sites.
• Perform ongoing data review/reconciliation activities and provide metrics and patient trackers to monitor data clean up.
• Perform database lock/unlock and freeze/unfreeze activities as appropriate for statistical review, interim analysis, and final database lock.
• Coordinate the archiving of study databases and related documents.
• Create Data Transfer Specifications and perform external vendor reconciliation.
• Perform SAE reconciliation.
• Work to maintain/improve client satisfaction by ensuring high quality service, communication and management of clinical data.

Data Manager – Inhouse

• Design and maintain the data architecture that underpins AI-powered field operations tooling — pipelines, semantic layer, and the clean interfaces that make operational data usable by LLMs and agents
• Build and deploy end-to-end tools: from raw telemetry and CRM data through to field manager-facing interfaces (dashboards, alerts, agentic workflows)
• Use agentic coding tools (Claude Code, Cursor, or equivalent) as part of your daily workflow — this role ships faster than traditional analyst roles because it builds differently
• Partner with data platform and BI teams to define where EFO data lives, how it gets modeled, and how it gets consumed — you own the field ops slice, not the whole warehouse
• Identify the 2–3 high-leverage use cases each quarter (coaching prep, schedule adherence, route deviation, performance/productivity metric visibility) and take them from concept to field-ready tool
• Instrument your own work: define success metrics, track field manager (FM) adoption, and present quarterly checkpoints to leadership
• Operate as an embedded IC — you have a seat at the director table, not a ticket queue

• Build Jobber’s Marketing Measurement System: Design and evolve an integrated marketing measurement system that brings together marketing mix modelling (MMM), attribution, incrementality testing, experimentation, forecasting, and business performance diagnostics
• Apply Advanced Analytics to Growth Decisions: Apply causal-inference and statistical techniques to distinguish true incremental impact from correlation, selection effects, seasonality, and noise
• Act as a Strategic Partner to Marketing Leaders: Partner closely with leaders and teams across Demand Generation, Performance Marketing, Brand Marketing & Communications, Marketing Operations, Finance, and adjacent analytics functions
• Build Scalable, AI-Native, and Trusted Analytical Capabilities: Create reproducible, well-documented analytical workflows with strong quality assurance, peer review, version control, and clear methodological standards
• Lead as a Senior Individual Contributor: Independently own complex, ambiguous, and high-impact work from initial problem framing through modelling, interpretation, recommendation, and business adoption.

Senior Business Data Scientist, Marketing

• Design, develop, test, and deploy scalable, production‑ready code and machine learning models.
• Transform large, complex datasets into actionable insights, recommendations, and data‑driven decisions.
• Develop innovative approaches to pattern recognition using machine learning, statistical, and analytical techniques.
• Design and deploy models to real‑time APIs, ensuring reliability, performance, and scalability.
• Communicate insights and model outcomes clearly to both technical and non‑technical audiences.
• Maintain, enhance, and optimize existing codebases and data pipelines.
• Drive delivery accountability for both project‑based initiatives and BAU work, aligned to agreed timelines and priorities.
• Ensure solutions conform to IT and Enterprise Architecture standards where applicable.

• Establish and champion CRM best practices across the Employer Canada team, developing frameworks, documentation, and training that enable sales leaders and team members to leverage Salesforce effectively and consistently
• Design and build intuitive dashboards and self-serve reporting capabilities that provide real-time visibility into key business metrics—including attainment, pipeline health, activity, and performance—enabling quick and accurate decision-making
• Automate manual reporting processes and routine data tasks using process automation and AI tools, reducing administrative burden and freeing up time for strategic analysis and business enablement
• Conduct ad-hoc data analysis specific to regional needs, translating complex data into actionable insights that support channel leadership strategy and drive informed business decisions
• Champion artificial intelligence adoption and innovation, identifying opportunities to apply AI tools and techniques to enhance reporting, forecasting, and business operations while fostering a culture of continuous learning
• Collaborate with sales leaders and the enablement team to identify data and reporting gaps, prioritize initiatives, and continuously improve our data infrastructure and self-serve capabilities.

Data Intelligence Lead, Senior Strategy Manager

• Support TELUS’ B2C business units on strategic initiatives to deploy or leverage data and technology responsibly and ethically
• Educate and advise business units on new or existing data uses and implementation of ethical technologies
• Lead innovative strategies and creative solutions to de-risk business activities and address privacy, compliance, AI and data governance considerations
• Actively engage and collaborate with cross-functional stakeholders to develop and implement technology solutions
• Lead the development, design, and implementation of processes, frameworks, technology requirements and testing protocols that standardize best practices in data governance
• Identify, quantify, and escalate data risks promptly, with a sense of urgency, and recommend mitigating controls
• Adapt to meet the changing needs of the organization, while maintaining a focus on the mission

Senior Data & Trust Manager – Privacy & AI

• Explore data sources and develop new PropertyTech and InsurTech models using data science, including machine learning and deep learning.
• Research, design, and implement deep learning algorithms and architectures that extract insights from imagery sources such as aerial imagery and geospatial data.
• Lead the development of high-quality datasets for robust model training and evaluation.
• Collaborate closely with cross-functional teams to understand requirements and translate product, engineering, and business constraints and questions into actionable data science problems.
• Coach and provide feedback to members of the Data Science and Machine Learning team, fostering skill development and knowledge sharing.

Senior Data Scientist – Computer Vision

• Deliver consulting projects focused on prospectivity analysis, geoscience data integration, and predictive modelling
• Collaborate with clients and internal teams to produce actionable exploration insights
• Process, analyze, and interpret geophysical, geological, geochemical, remote sensing, and drilling datasets
• Prepare high quality technical reports, presentations, maps, and client deliverables
• Support project proposal development, project planning, and technical scoping activities
• Ensure completion of timelogs/timesheets and business development activities

Geodata Scientist

• Lead delivery of advanced analytics and machine learning solutions for a large-scale transformation program within insurance practice.
• Bridge business objectives, data science execution, and delivery excellence.
• Remain deeply involved in model development and technical design.
• Coordinate with offshore team to ensure delivery quality.

Senior Data Scientist

• Translate business goals into analytical problems; Identify optimal algorithms, statistical techniques and/or GenAI architecture suitable for the business problem at hand
• Work in cross-functional teams to develop ML/data science products, including GenAI applications
• Apply best-in-breed data science techniques including descriptive, predictive, and machine learning methods from design to implementation
• Focus on feature engineering, model training, model evaluation, and prompt engineering for LLMs
• Design, develop, and deploy GenAI solutions including AI agents, chatbots, and autonomous systems that solve complex business problems
• Research and pilot the latest GenAI technologies, RAG (Retrieval-Augmented Generation) techniques, and agentic frameworks (LangChain, LangGraph, CrewAI etc.)
• Implement AI bots/agents with advanced reasoning capabilities and tool use patterns
• Work with vector databases and embedding models to build intelligent information retrieval systems
• Break down broader data science development milestones into actionable goals, activities, and work plans
• Create and maintain technical design artifacts describing application functionality, data models, interfaces, and integrations
• Engage and negotiate with stakeholders, make business recommendations with effective presentations of findings at multiple levels of stakeholders
• Champion continuous improvement and foster innovation within the analytics community

• Build/maintain anomaly detection model using statistical, machine learning, and data mining techniques.
• Design and implement AI-driven workflows to automate business intake processes, leveraging both low-code platforms (e.g., Copilot Studio) and code-based frameworks to enable intelligent comparison with historical requests and generate self-serve recommendations
• Partner with business stakeholders to understand objectives, gather requirements, and translate business needs into analytical solutions.
• Use programming languages (Python, Pyspark, SQL) to extract and prepare data, apply statistics and various advanced analytics along with business acumen to inform recommendations and insights from large datasets.
• Collaborate across AIML, data engineering, and analytics to deploy and monitor solutions at scale.
• Provide thought leadership and/or industry knowledge for own area of expertise and participate in knowledge transfer within the team and business unit.
• Share expertise and drive knowledge transfer through internal training, labs and runbooks

Data Scientist II

• Coordinate day-to-day delivery activities across Data Engineers and Power BI Developers.
• Translate project priorities into actionable work items and support backlog execution.
• Work closely with the Project Manager on planning, sequencing, and tracking of technical deliverables.
• Ensure alignment between technical work and project timelines, milestones, and dependencies.
• Identify and help resolve delivery risks, blockers, and dependencies.
• Contribute directly to development of data pipelines, transformations, and datasets as needed.
• Support implementation of data solutions aligned with established architecture and design.
• Assist in troubleshooting data issues and ensuring reliability of delivered solutions.
• Provide guidance to team members on implementation approaches, standards, and best practices.
• Support consistency in data modeling, pipeline design, and development practices.
• Participate in design discussions with the Data Architect and contribute practical input based on delivery experience.
• Support onboarding and mentoring of Data Engineers and Power BI Developers.
• Encourage knowledge sharing and continuous learning within the team.
• Provide input on performance, growth, and capability development.
• Work with business stakeholders as needed to clarify requirements and support delivery.
• Participate in working sessions, demos, and reviews to validate outputs.
• Ensure delivered solutions are aligned with business needs and usable for reporting and analysis.
• Promote good practices in data quality, validation, and monitoring.
• Identify opportunities to improve reliability, performance, and maintainability of data solutions.
• Support incremental improvement of data assets and processes.
• Support implementation of data governance practices, including metadata, lineage, access control, and data quality.
• Apply established standards and decision frameworks for shared KPIs, hierarchies, and reporting usage.
• Contribute to master data alignment (e.g., projects, equipment, locations, cost codes) in collaboration with the Data Architect and business stakeholders.
• Support implementation of security models (e.g., row-level security in Power BI) and ensure adherence to organizational, safety, and regulatory requirements.
• Maintain confidentiality with respect to Redpath business and vendor information.
• Support other members of the Corporate IT teams as required.

Data Team Lead

• The Senior AI Data Scientist is a senior individual contributor role within Data Science, AI, & Agentic. This role is responsible for leading complex analytical, predictive, and applied AI workstreams, operating with a high degree of independence and serving as a trusted advisor on data science and AI strategy.
• This role focuses on shaping and executing advanced modeling, experimentation, and applied AI solutions that address high-value business problems. The Senior AI Data Scientist translates ambiguous stakeholder needs into structured analytical approaches, model designs, evaluation frameworks, and actionable insights. They partner closely with cross-functional teams and stakeholders to ensure that analytical and AI-driven solutions are both technically sound and practically impactful.
• At this level, the role combines strong quantitative expertise with deep modeling capability, rigorous experimentation practices, and thoughtful application of AI methods. The Senior AI Data Scientist influences analytical direction, improves delivery quality through best practices and reusable approaches, and elevates the overall effectiveness of data science workstreams across engagements.

Senior AI Data Scientist

• Lead the end-to-end delivery of complex data and analytics programs
• Serve as the primary client-facing delivery leader
• Drive program planning, governance, risk management, stakeholder communication, and execution across distributed teams
• Collaborate closely with client teams, architects, engineering leads, and SMEs to deliver scalable data solutions
• Continuously improve delivery effectiveness by leveraging best practices
• Identify opportunities to expand engagements, drive additional value, and support business growth initiatives
• Foster a high-performance, collaborative team culture focused on accountability, innovation, and customer success

Delivery Manager – Data

• Lead the full AI product lifecycle: from identifying user pain points and defining KPIs to training foundation models and measuring real-world impact.
• Design and execute rigorous experiments (A/B tests, causal inference) to validate new AI features and drive product optimizations.
• Partner with Product Managers, Designers, and Engineers to translate business goals into a technical AI roadmap, ensuring our model development stays aligned with user needs and technical feasibility.
• Build and maintain production-grade training pipelines and evaluation harnesses using reproducible, well-tested code.
• Analyze production feedback and user interactions to iteratively refine models and maximize value.
• Foster a collaborative culture: mentor teammates, simplify complex problems, and drive iterative, high-impact delivery.

Senior Data Scientist, AI Products

• Analyze structured and unstructured data to generate actionable insights for clients.
• Build and maintain client facing products including but not limited to predictive models for market trends and revenue forecasts for publicly traded companies.
• Create visuals and narratives around data to shape conversations around trends within the data.
• Work with domain experts to develop features and test hypotheses relevant to equities and the overall advertising market.
• Partner with product, data engineering and sales teams to embed insights into solutions that align with customer needs.
• Ensure data integrity, quality, and governance in all analytical work.
• Stay informed of market trends, financial regulations, and modeling advancements relevant to advertising.

Data Scientist, Insights

• Leads a team of Digital Analysts in the translation of business requirements into technical deliverables
• Develops relevant processes, procedures, and standards within an analytics environment
• Supports Client Management and Analytics Execution teams in gathering and understanding web analytics needs and requirements
• Develops strategies and plans for supporting the achievement of business objectives
• As subject matter expert in Web Analytics, develops and recommends solutions and approaches
• Guides other Solution Delivery team members in performing technical and/or administrative duties

Team Lead, Digital Analytics

• Design, use and develop mathematical models for risk management and diligently prepare associated documentation
• Balance business needs, risk management, requirements and best practices
• Lead or contribute to projects, mandates or activities in your field of expertise
• Serve as a subject-matter expert to support various units and stakeholders across the organization
• Explain information in plain language and based on the target audience in order to facilitate comprehension and, if applicable, decision making
• Monitor the industry to understand and anticipate trends in your field of expertise to develop and update relevant best practices for the organization.

Data Scientist, Credit Risk Quantification

• Lead and manage all organization-wide master data governance activities.
• Improve the quality of data and enable the organization to leverage it as an enterprise asset.
• Oversee the execution of master data governance activities across all Saputo global divisions.
• Govern critical enterprise data domains (e.g., core master data, supply chain and operational data, and other key business data assets).
• Manage a team, setting and reviewing annual goals, assigning project responsibilities, coaching performance and supporting growth.
• Establish and govern an enterprise master data governance implementation roadmap.
• Define roles and responsibilities related to master data governance and ensure clear accountability.
• Facilitate the development of master data quality standards and adoption requirements.
• Define KPIs and quality metrics, monitoring compliance with standards and policies.
• Establish governance training programs for employees.
• Lead the global Master Data Governance council and other related committees.

Data Scientist jobs in Canada

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Lead Data Scientist – Growth & Experimentation

Data Manager – Inhouse

Data Scientist

Senior Business Data Scientist, Marketing

Data Scientist

Data Intelligence Lead, Senior Strategy Manager

Senior Data & Trust Manager – Privacy & AI

Senior Data Scientist – Computer Vision

Geodata Scientist

Senior Data Scientist

Senior Data Scientist

Data Scientist II

Data Team Lead

Senior AI Data Scientist

Delivery Manager – Data

Senior Data Scientist, AI Products

Data Scientist, Insights

Team Lead, Digital Analytics

Data Scientist, Credit Risk Quantification

Senior IT Manager, Data Governance