Top Data Jobs in Ottawa

Design, build, and operate production ML systems that generate trusted signals for ranking, retrieval, recommendations, propensity/churn/LTV, and next-best-action decisioning. Define signal/data contracts, own feature and candidate generation through serving, experimentation, monitoring, and feedback loops, and evaluate long-term business impact, trust, fairness, and compliance. Partner across product, data, modeling, risk, and compliance and apply AI/agents to accelerate engineering and operations.

As a Senior Data Scientist, you will lead AI risk assessments, coordinate with stakeholders, and ensure effective AI governance practices for safe financial application deployment.

The GTM Lead for Insurance will build and manage enterprise insurance accounts, develop relationships with senior industry leaders, and shape sales strategies for AI solutions.

The Staff Software Engineer will lead the development of backend services and data pipelines for real estate MLS data, mentor teams, and work on AI integration.

This role involves deploying solutions for clients, managing data compliance, and providing feedback for product improvement. Requires collaboration with teams and clients.

Support end-to-end data solution delivery: gather requirements, analyze and profile data, build models and visualizations (Power BI/Oracle), support UAT/testing, and collaborate with stakeholders to produce actionable insights and maintain data governance.

Wayvia is building a talent pipeline for engineering roles focusing on backend systems, data pipelines, and product engineering. They seek curious and collaborative engineers to connect with and solve future challenges.

The Senior Software Engineer will manage data systems, develop scalable pipelines, ensure data security, and build self-service applications for users at Coinbase.

Own and deliver GDMS secure AI, GenAI, and data platform in AWS. Define roadmap, reference architectures, and paved roads for Bedrock and SageMaker-based GenAI and MLOps. Implement secure-by-default patterns, IaC/CI-CD automation, monitoring, governance, and operational runbooks while driving adoption and cross-functional delivery.

Analyze and document business and system requirements for an Intelligence Mission Data project; facilitate workshops and use-case development; produce system specifications and traceability; perform technical options analysis and solution evaluations; plan organizational impact and integration; participate in testing; and prepare briefings, reports, and meeting artifacts.

Design and maintain scalable ELT/ETL pipelines on GCP, build and evaluate predictive ML and NLP models (including LLM prompt engineering), implement XAI for interpretability, perform EDA and automation, and document governance and compliance to support enterprise fraud detection and SIU stakeholders.

Lead a team of data engineers and analysts, define the data engineering roadmap, oversee data infrastructure, ensure analytics delivery, and establish data governance standards.

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.

Lead the PoC data science team, delivering POCs for enterprise clients, mentoring team members, and driving fraud analytics and ML solutions.

As an Intermediate Data Engineer, you will develop data infrastructure and products, collaborate with diverse teams, and enhance technical quality across the data team.

Lead Andela Academy, scaling enterprise learning solutions in AI and Cloud. Build relationships, oversee team delivery, and drive revenue growth with high client satisfaction.

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.

Lead and grow combined data engineering and AI teams, build data platform standards (Snowflake, dbt, Airflow, Terraform), develop and productionize ML and LLM solutions (RAG, fine-tuning, vector DBs), partner with cross-functional stakeholders, and establish model governance, observability, and data quality to drive AI-powered underwriting and pricing.

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.

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.

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.

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.