Top Data Jobs in Ottawa

Research, evaluate, and manage partnerships with data providers. Assess datasets for coverage and interoperability, support deal structuring, maintain partnership pipeline, and collaborate with product, engineering, and legal to integrate data into the platform.

Build and operate scalable data ingestion, transformation, and connector frameworks; design and maintain a knowledge-graph-based data fabric; normalize and vectorize enterprise data for LLM/AI workflows; implement governance, lineage, access controls, and secure APIs to serve ML/agent pipelines.

As a Software Engineer in Data Infrastructure, you will build and maintain high-performance data storage systems for AI training, collaborate with experts, and optimize data processing.

Design business data models and data architectures, mentor data teams, translate business requirements into technical specifications, and set standards for AI-augmented development practices.

Build scalable ML data pipelines for Waabi's autonomous driving platform. Design, optimize, and manage datasets and training processes while collaborating with scientists and engineers.

Design, build, deploy, and maintain production-grade measurement and ML systems (MMM, incrementality, insights, recommendations). Translate statistical and causal methods into scalable products, debug full-stack data and model issues, evaluate methodology tradeoffs, and collaborate with engineering, product, and customer teams while providing customer-facing technical support.

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 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.

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 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 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.

Audit and ensure quality of data labeling outputs, coach and onboard annotators, enforce and improve guidelines, identify recurring issues and edge cases, escalate risks, and document feedback to support human-in-the-loop AI workflows.

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.

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

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.

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 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.

As a Staff Software Engineer, you'll architect and build foundational platform services, focusing on data infrastructure, healthcare interoperability, and secure APIs while mentoring other engineers.

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.

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.

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.