Top Data Jobs in Ottawa

Evaluate and review AI outputs, generate or assess content, and ensure cultural relevance and accuracy in various media for AI projects.

Lead and scale the Growth engineering organization at MaintainX, focusing on technical strategy, product-led growth, and enhancing cross-functional initiatives.

Lead Data Acquisition Team, drive AI-augmented development, establish specification-driven methodology, oversee data pipeline architecture and ensure operational excellence.

Support GTM analytics by analyzing Sales, Revenue Operations, and Customer Success data; build and maintain dashboards and recurring reports; perform ad-hoc analyses and experimentation evaluation; define and maintain reliable metrics with analytics teammates; and document and communicate insights to technical and non-technical stakeholders.

Lead enterprise data architecture and modeling to consolidate legacy court reporting marts into a centralized EDW. Design data models, ETL processes, data governance, and Power BI reporting. Provide architecture alignment with GAEA, conduct risk assessments, mentor teams, and support migration and implementation activities.

Lead and scale a product-focused Data Science team to drive acquisition, engagement, retention, and member health outcomes. Set DS strategy, measurement frameworks, operating model, and prioritize high-impact analytics. Partner cross-functionally, ensure rigorous, actionable analyses, apply experimentation, causal inference, segmentation, forecasting and personalization, and collaborate with data engineering to improve data foundations.

Audit and ensure quality of data labeling work for ML using human-in-the-loop processes. Provide feedback, train and support annotators, monitor workflows, identify recurring issues, and help improve guidelines and processes.

Audit and review data labeling outputs, provide actionable feedback, train and support labeling teams, monitor workflows, identify recurring issues and edge cases, participate in calibrations and stakeholder syncs, and help improve guidelines and processes in human-in-the-loop AI workflows.

Audit and improve data labeling quality in human-in-the-loop ML workflows. Coach and onboard labelers, provide actionable feedback, monitor queues, identify recurring issues, and help refine guidelines and processes.

Evaluate AI model outputs, perform audit-style reviews to find patterns and failure modes, apply and refine labeling/evaluation guidelines, document findings, communicate insights to leads and stakeholders, and participate in team calibrations to improve model evaluation and data quality.

Audit and ensure quality of labeled data by reviewing DLA outputs, providing feedback and coaching, supporting onboarding and training, monitoring workflows, identifying recurring issues and edge cases, and contributing to guideline and process improvements in human-in-the-loop AI workflows.

Evaluate AI model outputs, perform audit-based reviews, apply and improve guidelines, identify trends and failure modes, document findings, and collaborate with leads and cross-functional teams to refine quality standards.

Audit and ensure quality of data labeling outputs, coach and onboard annotators, apply and refine guidelines, monitor workflows and escalate issues, document findings, and support human-in-the-loop AI evaluation processes.

Evaluate AI model outputs, perform audit-based reviews, identify failure modes and trends, apply and improve guidelines, document findings, and collaborate with leads and cross-functional teams to refine quality standards.

Audit and ensure quality of data labeling outputs, coach and onboard Data Labeling Associates, provide actionable feedback, monitor workflows, identify patterns and edge cases, and help improve guidelines and processes in human-in-the-loop AI workflows.

Evaluate AI outputs and audit model behavior, apply and refine guidelines, identify failure modes and trends, document findings, and collaborate with leads and cross-functional teams to improve evaluation frameworks and data quality.

Evaluate AI outputs, perform audit-based reviews, identify patterns and failure modes, apply and improve annotation/evaluation guidelines, document findings, and communicate insights to cross-functional teams to improve model performance and evaluation frameworks.

Evaluate AI outputs, perform audit-based reviews, identify failure modes and trends, apply and refine guidelines, communicate findings, participate in calibrations and stakeholder syncs, and help improve evaluation frameworks for human-in-the-loop AI.

Audit and monitor data labeling outputs, provide actionable feedback, support onboarding and training of annotators, enforce and improve guidelines, escalate workflow issues, and document findings to maintain high-quality human-in-the-loop AI data processes.

Evaluate AI outputs, audit model behavior, apply and refine labeling guidelines, identify failure modes and trends, document findings, and collaborate with cross-functional teams to improve model evaluation and data quality.

Perform rating and annotation tasks for AI development following project guidelines. Maintain accuracy, meet deadlines, communicate in French-Canadian and English, and complete research within short throughputs. Temporary freelance role, 15–20+ hours/week, remote.

Evaluate AI model outputs, perform audit-based reviews to identify patterns and failure modes, apply and refine guidelines, document findings, and collaborate with leads and cross-functional teams to improve model evaluation and data quality.

The Senior Web3 Data Analyst will manage web3 analytics, monitor on-chain activity, analyze competitor metrics, and deliver insights for product and leadership teams.

Own cloud operational health, architecture intelligence, and cost optimization. Analyze cloud spend, build dashboards/reporting, enforce governance, review architecture for cost impact, and translate technical findings for engineering, finance, and executives.

Build and scale AI data and ML infrastructure: evaluation systems, fine-tuning pipelines, agent-first product surfaces, high-throughput data workflows, and integrations of new models into production.