Top Data Jobs in Ottawa

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.

The Data Engineer will analyze and interpret data to drive insights, create dashboards, and enhance product performance, collaborating with cross-functional teams.

As a Data Analyst, you will analyze user behavior and product performance, creating reports and dashboards to aid decision-making and optimize user experience.

As a Python and Kubernetes Software Engineer, you will develop data analytics solutions using open source tools, collaborating with a distributed team.

Lead a team in developing scalable data solutions, mentor engineers, manage expectations, and contribute to code. Focus on high-quality software practices.

Design and run evaluation frameworks to assess third-party vendor data for fraud detection. Build lift analyses, backtests, and champion/challenger tests; profile vendor datasets for quality and coverage; collaborate with fraud, product, and data engineering to define ingestion and testing requirements; translate findings into actionable recommendations and maintain an internal knowledge base; support ad hoc analyses on fraud trends and model performance.

Lead enterprise Data & AI transformation initiatives, managing the delivery of cloud-based analytics platforms while aligning business and technology objectives. Responsibilities include program governance, stakeholder management, and overseeing implementations of Microsoft Fabric.

Design, build, and optimize cloud-based data platform components: ingest and transform varied datasets, create metadata-driven pipelines and data APIs, support AI/ML infrastructure, implement monitoring and metrics, and improve data processes to enable analytics and operational systems.

The Senior Data Scientist will build frameworks for data-driven decision making, develop predictive models, ensure data quality, and enhance customer experiences through analytics and operational insights.