Top Data Jobs in Ottawa

Lead data-driven underwriting improvements by analyzing claims, risk signals, and underwriting data; build statistical and ML models for cyber risk assessment, pricing, and automation; produce dashboards and reports; collaborate with actuarial, product, and engineering teams; mentor teammates and lead cross-functional initiatives to optimize underwriting and risk selection.

Lead labeling quality and methodology for ML at Coalition: design annotation tasks, define quality frameworks (IAA, gold standards), manage Label Studio and vendor BPOs, partner with ML/data teams, and track operational metrics to improve accuracy and throughput.

Deliver front-office investment technology consulting: requirements, workflow design, system configuration, integration, testing, and handover. Provide subject-matter expertise on OMS/EMS/PMS platforms, data architecture, cloud integrations, and program governance to ensure timely, on-budget transformations.

The Data Engineer will build and manage scalable data pipelines and databases, collaborate on architecture, and drive data systems towards real-time capabilities in a fast-paced, innovative environment.

The Data Analyst will support business needs through technical problem-solving and data analysis, focusing on improving user experience and conversion rates in a B2B2C environment.

Design, build, and operate a cloud-native data platform and ML infrastructure at scale. Own end-to-end pipelines from CDC to feature stores, model training/serving, monitoring, and data product SLAs. Apply distributed systems and MLOps practices, leverage LLMs/agentic systems, and translate technical issues to business impact.

As a Data Scientist at Sardine, you'll design and deploy data-driven solutions to prevent fraud, collaborating with clients and engineering teams to build and optimize machine learning models.

Lead analysis of customer, digital, and transportation data to build targeting, pricing, and optimization models. Design and run customer analytics (price elasticity, churn, LTV, segmentation), mentor junior data scientists, lead stakeholder projects, and support data storage planning and cross-department representation to drive actionable, data-driven business decisions.

Drive product and performance analytics for mobile app user acquisition: define core metrics, analyze campaign and product trends, build dashboards and data pipelines, support experiments and ML teams, automate reporting (including AI-powered workflows), and communicate findings and recommendations to stakeholders.

The Product Owner will define and prioritize backlog items for Autodesk's AEC Platform Data team, focusing on information requirements management and collaborating with clients and cross-functional teams.

Seeking Data Platform Engineers to design, build, and manage scalable data pipelines and analytic query engines while ensuring data integrity and performance optimization.

Own and architect the semantic layer and metrics/metadata for the data platform (Databricks/GCP). Build AI-ready models, enforce modeling and documentation standards, implement observability/testing for pipelines, enable self-serve analytics, and collaborate across product, clinical, and business teams to translate questions into reliable data solutions.

Lead and build Burq's data and ML platform: own warehouse, ingestion, semantic layer, and BI; architect and ship forecasting, optimization, matching, and reliability models; implement feature pipelines, training/serving, and monitoring; drive AI agent initiatives; hire and grow a data/ML team; set governance, intake processes, and partner with product, engineering, and commercial stakeholders to align models with business impact.

Build and maintain high-performance, fault-tolerant distributed backend services and data pipelines for large genomic and health datasets. Design storage, indexing, and async event workflows; optimize latency, throughput, and reliability. Partner with bioinformatics and product teams to validate scientific accuracy and guard AI-driven responses.

The Data Migration Specialist will manage end-to-end data migrations, ensuring data quality and effective communication with clients and internal teams while resolving challenges.

Lead architecture and hands-on engineering of Pantheons data platform: extend ingest, curation, and retention services; build ML/LLM-ready data infrastructure; mentor engineers; operate in a DevOps model; ensure data governance, observability, reliability, and cross-functional partnership.

Build and maintain the product analytics data foundation: design scalable dbt models, define semantic models and governed metrics, partner with Engineering on instrumentation, certify datasets for BI tools, monitor pipelines and data quality, and use AI to accelerate analytics engineering workflows.

Design, build, and scale cloud lakehouse data products (Databricks/Snowflake) from ingestion to analytics-ready datasets using medallion architecture. Develop reusable Python frameworks, Spark pipelines, and dbt/SQL transformations. Implement data modeling, governance, observability, validation, and SLA monitoring. Troubleshoot production issues, adopt AI to accelerate engineering, and provide technical leadership, code reviews, architecture decisions, and mentoring for global engineering teams.

Own and operate backend and data services for a cybersecurity platform: design and run high-volume streaming ingestion, ETL/ELT pipelines, lakehouse/warehouse modeling, APIs, and observability. Mentor engineers, contribute across TypeScript/Python/Go microservices on Kubernetes, ensure performance/scale/security, and evaluate AI tools to accelerate development.

Partner with founders and cross-functional teams to analyze financial and operational data, build executive dashboards, create P&L and margin analyses, forecast and model unit economics, identify cost leaks, reconcile transactions, and present recommendations to improve profitability, cash flow, and business performance.

Own and scale end-to-end physical-data collection operations: scheduling, operator utilization, station uptime, throughput and quality. Recruit and develop operators and shift leads, standardize playbooks and SOPs across sites, partner with Quality and Annotation teams, stand up partner-site deployments, and build reporting that tracks collected hours, quality, and cost.

Own and scale the pipeline that converts raw robot demonstrations (trajectory/video) into training-ready datasets. Design ontologies, run and automate annotation operations (internal and vendor), apply vision-language models for labeling and synthesis, track quality metrics, and close the loop from eval failures to targeted data collection to improve the model.

Design, build, and maintain petabyte-scale batch and streaming data pipelines and core data infrastructure for robotics foundation model training. Own data models, storage, ingestion, transformation, and orchestration. Standardize pipelines across teleoperation and simulation datasets and collaborate with cross-functional team to support Physical AI development.

Build and run a combined sourcing and vendor-management function for data and compute. Source datasets and partners, negotiate contracts (MSAs/SOWs), forecast and secure compute capacity, run pilots and quality benchmarks, own data rights and compliance with Legal, and create scorecards, tooling, and metrics to manage spend, utilization, and supplier performance.

Lead product analytics by building scalable data models and measurement frameworks, owning dbt workflows, performing cohort/retention and funnel analyses, defining KPIs, automating analytics, and presenting recommendations to influence product and business decisions.