Top Data Jobs in Ottawa

Develop data-analysis pipelines bridging ultrasound, synthetic biology, imaging and sequencing. Collaborate with wet-lab and ML teams to define objectives, encode priors, run ML pipelines for de-novo design and closed-loop active learning, and produce actionable insights to accelerate molecule discovery and device optimization.

Build and maintain financial and operational datasets and Power BI dashboards; map metrics across eCommerce and engineering sources; support AOP, ARR, pricing and retention modeling; run CI/CD pre-production checks, monitor processes, and partner cross-functionally with Finance, Marketing/Retention, and Product.

Lead architecture and implementation of agentic AI systems powering Zuma's platform. Rebuild onboarding and integration frameworks, implement analytics and observability for LLM performance, translate product needs into technical solutions, mentor engineers, establish engineering standards, and work directly with customers to enable scalable, production-grade AI experiences.

Perform L3 network troubleshooting and RCA on production enterprise networks, review telemetry/logs/configs, label and annotate network behaviors for ML training, and help define schemas and data pipelines to support AI-driven infrastructure.

The AI-Native Data Engineer will build data pipelines, architect databases, design GCP infrastructure, and automate workflows for energy management using AI.

Lead enterprise data, analytics, and AI transformation engagements as a strategic advisor and architect. Design cloud data platforms, warehouses/lakehouses, governance, BI/semantic models, and integration strategies. Provide delivery oversight, mentor teams, conduct architecture reviews, and support pre-sales and thought leadership to drive measurable business outcomes.

Review and analyze news content from patient advocacy groups, extract insights, and input data into Zebricks to support AI/ML processes. Collaborate with the team on data projects, shape data best practices, and perform web research and spreadsheet-based data work.

As a Data Engineering Architect, lead the transformation of data engineering into a modern platform, oversee architecture and technical leadership, design scalable systems, ensure data quality and governance, and mentor teams for a data-driven culture.

The Enterprise Solutions Specialist drives client success through project management and operational oversight, ensuring scalable processes and effective communication between teams. This role involves direct client engagement and supports automation initiatives.

As a Staff Data Scientist, you will analyze large-scale broadband data, develop and validate ML models, and integrate solutions into Calix Cloud, collaborating with teams for enhanced network intelligence and subscriber experience.

Develop, evaluate, and deploy machine learning models for risk scoring, threat intelligence, and vendor risk assessment. Perform exploratory data analysis on large datasets, validate models for accuracy and scalability, and integrate models into production with engineering and product teams. Communicate findings to technical and non-technical stakeholders and stay current on research and methods.

Lead design and delivery of cloud-based data architectures and ETL/ELT pipelines for marketing analytics. Advise clients, integrate marketing data, build and optimize pipelines and reusable models, ensure data quality/security, mentor team members, and align technical solutions to business goals.

Serve as delivery lead and senior digital analyst for clients: gather measurement requirements, analyze large event datasets (BigQuery/SQL), build visualizations, present insights, guide implementations (GTM/Tag tools), coordinate cross-functional delivery, and ensure QA and on-time execution.

Annotate and manage training/test datasets, run model training, evaluate model performance, perform manual QA and error analysis, and refine annotation guidelines for NLP/ML projects.

Design, build, and support data services, pipelines and internal data architecture. Collaborate on product development, analyze and organize raw data, improve data quality, define best practices, and identify acquisition opportunities to support business insights and member-facing services.

Analyze business performance and consumer behavior, define and track KPIs, perform deep dives and trend analysis, build dashboards and reports, partner with business leaders to shape consumer strategy, and present actionable insights.

Lead product marketing for Procore AI & Data products: own positioning, messaging, launches, competitive positioning, sales enablement, and cross-functional GTM programs. Collaborate with Product, Demand Gen, Sales, and global Marketing to create customer-facing content and drive adoption.

Lead enterprise data architecture across AWS and Databricks Lakehouse: design scalable data models, ensure governance and data quality, mentor teams, evaluate and adopt data technologies (Delta Lake, Unity Catalog), oversee large projects and deliverables, and incorporate generative AI to accelerate data modeling and delivery.

Lead growth-focused data science across cross-functional squads: design experiments, build models and dashboards, run causal analysis and forecasting, define KPIs, identify high-leverage opportunities, and drive data-informed product and marketing decisions to improve acquisition, retention, and monetization.

Lead growth-focused data science within a cross-functional squad: define KPIs, run experiments, perform causal inference and forecasting, build dashboards and self-serve tools, identify high-leverage opportunities across acquisition, activation, retention and monetization, and evangelize data-driven decision making to drive measurable business impact.

Collect, clean, validate, and analyze data to identify trends and business opportunities. Create reports and dashboards (Excel, SQL, Power BI/Tableau), monitor KPIs, document data sources/processes, and present insights to stakeholders while collaborating across teams.

Design, build, and maintain scalable data ingestion pipelines, databases and data lakes for credit and lending datasets. Ensure accurate data mappings, monitoring and alerting, translate stakeholder requirements into analytical data flows, and integrate APIs to deliver reliable, high-quality data products.

Design, build, and maintain reliable data pipelines and lakehouse assets for batch and streaming workloads. Implement ETL/ELT in Python and SQL, orchestration and monitoring (Airflow/Kubernetes), and retrieval/RAG/vector search pipelines for LLM and AI applications. Ensure data quality, lineage, versioning (Delta/Parquet/Iceberg), cost/performance optimization, and collaborate with MLOps and sports data teams.

Design and implement real-time MAC data-plane software for high-throughput satellite links. Build packet-processing pipelines, queue and buffer management, retransmission and loss recovery, and integrate with PHY, modem, FPGA, DMA, and SoC hardware. Optimize for low latency and high throughput using zero-copy, scatter-gather, lock-free structures, and hardware offload. Lead architecture through prototyping, hardware integration, performance qualification, and operational deployment while diagnosing timing and synchronization issues.

Lead development and evolution of AlphaSense's financial data taxonomy and generation rules. Serve as subject-matter expert, align cross-functional teams, define requirements for new datasets, ensure AI/LLM compatibility, maintain documentation, and build scalable frameworks to expand and maintain high-quality structured financial data for capital markets users.