Top Data Jobs in Ottawa

Lead Kraken's Data Platform team focused on real-time infrastructure for data systems, managing team dynamics, architecture design, and promoting AI automation.

The VP of Data & AI Engineering leads Duck Creek's enterprise data and AI strategy, overseeing global teams, implementing scalable platforms, and ensuring data governance and responsible AI practices.

Lead data-driven strategy by partnering with senior executives, design and deliver sophisticated analytical solutions on large-scale data, build trusted cross-functional relationships, and mentor data scientists to raise analytical quality across the organization.

Lead data-driven strategy by partnering with senior executives to deliver rigorous analytical solutions. Design and implement sophisticated analyses on large-scale data, build trusted cross-functional relationships, mentor data scientists, and influence long-term product and business decisions.

Lead the Data Platform product for AISim, owning ingestion, schema reconciliation, lineage/provenance, sensitivity and field-of-use governance, federated data sharing, experiment-integration APIs, and dataset usability metrics. Partner with engineering, science, design, and compliance to turn ambiguous data problems into measurable, scalable platform features.

Own and evolve ClickHouse's Python connectors and SDKs; build integrations with orchestration and transformation tools; design AI/LLM connector patterns for RAG, vector search, and feature pipelines; engage with open-source community; collaborate across product and engineering to prioritize and ship production-grade data integrations.

Embedded on a core business team, define the right problems, build metrics and reports, run analyses and experiments, surface actionable insights, and drive data-informed decisions end-to-end across growth, product, AI, or partnerships.

Embed within a core business area to define problems, build metrics, run analyses and experiments, surface insights, and drive data-informed decisions end-to-end across growth, product, AI/automation, or partnerships.

Embedded data scientist owning analytics for a business unit: define problems, build metrics/reports/experiments, surface insights, and drive data-informed decisions across growth, product, AI, and partnerships.

Embed with a core business area to define problems, build metrics/reports, run analyses and experiments, and drive data-informed decisions end-to-end. Translate ambiguous questions into clear hypotheses, surface insights, and partner with product, growth, finance, and ops to execute recommendations and deliver measurable business impact.

Embedded on a central Data Science team, you will define problems, build metrics/reports, run analyses and experiments, surface insights, and drive decisions end-to-end for growth, product, AI/automation, or partnerships.

Embedded on a business unit, define analytic questions, build metrics/reports, run analyses and experiments, surface insights, and drive data-driven product, growth, AI, or partnership decisions end-to-end.

Lead architecture and delivery of production-grade machine learning systems, mentor global ML engineers, drive model lifecycle (training, deployment, monitoring, retraining), and apply advanced ML (fraud detection, recommendation, churn/LTV) with MLOps and inference optimization.

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.

Support client engagements measuring de-identification and re-identification risk for datasets (especially healthcare). Validate and prepare data, perform qualitative and quantitative risk analyses, document results in client-facing reports, prepare presentations/materials, assist deliveries and remote coaching, and develop expertise in privacy regulations and statistical disclosure control.

Lead design and delivery of end-to-end ML solutions for insurance (claims, underwriting, sales). Own feature engineering, model selection, training, validation, and handling imbalanced/weak-label data. Ensure explainability, governance, and production readiness. Act as client-facing data scientist, coordinate offshore teams, contribute to MLOps, model monitoring, and deployment on cloud analytics platforms.

The Software Engineer will provide technical support to customers, collaborate with engineering teams, and drive customer success initiatives while leveraging Python and SQL expertise.

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 role involves designing scalable data pipelines for 3D, video, and sensor data, optimizing infrastructure, and productionizing ML models with researchers.

As a Data Annotation Specialist, you will label, audit, and optimize data for AI models in Chinese while ensuring high accuracy and quality. You will perform repetitive tasks and collaborate with teams, providing feedback and recommendations for data improvement.

The Junior Data Scientist will design and develop data models using Machine Learning and Analytics for trading and compliance in finance.

As a Software Engineer, you will create tools and pipelines for AI development, manage data solutions for ML teams, and ensure high-quality code.

The Data Platform Marketing Manager will lead marketing strategies for DoiT's data lake and Snowflake optimization business, aiming to drive growth and impact.

Build and operate a reliable data platform: monitor infrastructure and pipelines, automate platform tasks, manage IaC and CI/CD, support RBAC and governance, contribute to data pipelines and migration, document runbooks, and participate in on-call incident response while improving AI-assisted engineering workflows.

Lead the data and ML product vision: define roadmap, prototype models on noisy behavioral datasets, translate signals into user-facing features, partner with engineering to productionize ML, and generate insights to inform product roadmaps.