Top Data Jobs in Ottawa

Intern will build enterprise data foundations and AI agents connecting LLMs to authoritative business knowledge. Responsibilities include developing retrieval/RAG pipelines, agent workflows, persistent memory/state management, governance controls, auditability, and integrations with vector DBs, knowledge graphs, enterprise tools, and multiple LLM providers.

Use customer, digital, and transportation data to build targeting, propensity, pricing, loyalty, and churn models; run experiments (A/B), optimize promotions and pricing, present insights to stakeholders, manage data structures in Databricks/AWS, and lead project work with business partners to drive data-driven decisions.

Lead analytics for Internal Audit Claims: extract, transform, reconcile, and validate large claims datasets; build and maintain Power BI reports/dashboards; perform QA and tie-outs; support automation, bots/AI enablement, and executive reporting; mentor other analysts and partner with audit, IT, and business stakeholders to deliver repeatable, validated analytics.

Manage relationships with strategic healthcare clients, oversee complex projects, innovate operational processes, and lead large teams to improve AI training and application.

Own analysis and algorithmic solutions for ads delivery; train, deploy, and scale ML/optimization models into production; build logging, monitoring, and lineage; improve data pipelines; mentor data scientists; use agentic AI tools daily; collaborate with engineers to integrate models into ads systems and handle on-call production incidents.

Analyze player behavior, game health, engagement, retention, monetization, and live-service performance for Marvel Strike Force. Own KPI reporting, explain trends and variances, evaluate experiments, validate analytics tracking, and deliver actionable insights to product, design, marketing, live operations, and leadership teams. Use SQL, Python, reporting tools, and AI-enabled workflows while maintaining data quality, privacy, and analytical rigor.

Audit and improve human and model-generated data outputs for accuracy, consistency, and policy compliance. Provide actionable feedback, clarify evolving guidelines, support annotators, coach new team members, monitor workflows and blockers, identify recurring issues and edge cases, participate in calibration discussions, and document findings to improve team performance and quality at scale.

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

Own the end-to-end analytical function for a business area such as growth, product, AI and automation, or strategic partnerships. Define business questions and metrics, build reports, analyze data, design experiments, and deliver actionable insights. Partner with product, growth, finance, and operations leaders to influence decisions and drive measurable outcomes. The role requires strong analytical ability, independent problem-solving, clear communication, and ownership of ambiguous initiatives.

Own the analytical function for a business unit across growth, product, AI and automation, or strategic partnerships. Define business questions and metrics, build reports, analyze data, design experiments, and deliver actionable insights. Partner with product, growth, finance, and operations leaders to communicate findings, influence decisions, and drive measurable outcomes. The role requires strong analytical problem-solving, independent execution, and persuasive communication.

Own end-to-end analytical work for a business unit spanning growth, product, AI and automation, or strategic partnerships. Define business problems and metrics, pull and structure data, build reports, run analyses, design experiments, communicate insights, and drive recommendations and decisions with cross-functional stakeholders. Requires strong analytical thinking, independent execution, persuasive communication, and experience solving ambiguous problems in consulting, investment banking, or high-growth technology.

Own end-to-end data science for a business area such as growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; generate actionable insights; and influence product, growth, finance, and operations leaders. The role requires independent analytical execution, first-principles problem solving, persuasive communication, and ownership of ambiguous initiatives that drive measurable business outcomes.

Own the analytical function for a business unit spanning growth, product, AI and automation, or strategic partnerships. Define business questions, metrics, reports, analyses, and experiments; generate insights; and drive decisions and measurable outcomes with cross-functional stakeholders. The role requires independent data work, strong problem-solving, persuasive communication, and ownership of ambiguous initiatives from end to end.

Own and evolve high-throughput backend services that deliver machine learning-powered predictions to merchants. Responsibilities include API design, service decomposition, Python development, PostgreSQL and Kafka integration, distributed systems reliability, observability, feature pipelines, ML serving, model deployment, versioning, rollback, and prediction monitoring. The engineer will partner with Data Science, lead architectural decisions and technical reviews, mentor teammates, and establish strong engineering practices for production data products.

As a Data Analyst at Stripe, you'll build data products, metrics, and dashboards, analyze large datasets, and communicate insights to drive business decisions.

Contributors record authentic first-person video of household tasks, hands-on work, and everyday activities using a phone mounted to a headstrap. They choose their own schedule, preferably recording at least 10 hours weekly, then upload footage through Perle’s app for review and payment. Contributors must be at least 18, reside in the United States, Canada, or Latin America, have a supported smartphone, and be comfortable wearing a headstrap.

Analyze microbiome and longitudinal clinical datasets, develop microbiome metrics and biomarkers using statistics and machine learning, create visualizations and reports, draft clinical research documents, support product and study development, and contribute to scientific publications and proposals.

Design and optimize data pipelines, cleanse and transform datasets, and build reliable reporting and analytics solutions using PostgreSQL, Metabase, and related tools. Collaborate with stakeholders to define KPIs, develop dashboards, troubleshoot data processes, and deliver actionable insights that support data-driven decisions.

The role involves designing and building data pipelines for analyzing user feedback on AI models, ensuring data quality and infrastructure scalability.

As a Data Engineer, you will develop and manage data pipelines, ensuring reliable data ingestion and delivery while optimizing for simplicity and performance.

Design and maintain data warehouse systems, develop ETL pipelines, manage datasets for analytics, and implement real-time data solutions.

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.

Lead design and implementation of a federated Lakehouse data platform, unify data silos, define governance/observability, enable secure AI/ML (RAG/vector readiness), mentor architects/engineers, and present roadmaps to executives.

The role involves programming and data review for real-world data analysis, supporting epidemiologists, and managing complex data sets using statistical programming languages and tools.

The Data Analyst will build key data models and metrics for decision-making, partner with stakeholders for analytical insights, create datasets for self-service reporting, and ensure data reliability through quality checks.