Top Data Jobs in Ottawa

Leads Medavie’s enterprise data, analytics, and AI strategy and program governance. Defines the multi-year roadmap, strategic guardrails, portfolio priorities, governance forums, decision rights, reporting, metrics, and value-realization practices. Partners with executives, business lines, IT, risk, privacy, and legal teams to align investments, manage strategic risks, promote responsible AI, and advance data and AI literacy across the organization.

Coordinate cross-functional programs across HR, Finance, and corporate functions from planning through execution. Manage employee lifecycle workflows, recruiting operations, compliance tracking, finance operations, audits, budgets, reporting, and leadership updates. Identify and implement AI-enabled workflows, translate processes into agent instructions, monitor accuracy and privacy, establish performance metrics, and train internal users. Partner with executives and functional leaders to improve operational processes and adoption of automation.

Evaluate and annotate 3D meshes, GLB files, images, and short videos for model training and error resolution. Identify whether assets are artist-created or AI-generated, and detect geometry defects, topology errors, and scene inconsistencies. Requires hands-on 3D modeling experience, familiarity with Blender or Maya, strong mesh-topology knowledge, English proficiency, and a suitable computer with dedicated graphics capabilities.

Design and lead scalable data engineering solutions using Databricks and Azure. Establish enterprise data modeling and engineering standards, architect pipelines and integrations, improve platform reliability and performance, guide modernization and cloud migrations, troubleshoot complex issues, and mentor data engineers while ensuring security, quality, and governance.

Design, develop, and maintain scalable data pipelines, models, ingestion, transformation, and integration solutions using Databricks and Microsoft Azure. Troubleshoot performance and data quality issues, optimize workflows, support migrations and modernization, and collaborate with architecture and engineering teams. Translate business requirements into technical solutions while following security, governance, quality, and engineering best practices.

Build and maintain highly available, scalable distributed systems and real-time data services supporting Internet intelligence products. Responsibilities include developing data pipelines, messaging systems, databases, caching layers, APIs, tooling, libraries, and cloud or on-premises services; productionizing prototypes; improving reliability and maintainability; and collaborating with engineering, product, machine learning, and security research teams.

Build highly available, scalable distributed systems and real-time data services supporting Internet-wide scanning, DNS resolution, certificate ingestion, security research, and customer applications. Responsibilities include developing data pipelines, messaging systems, databases, caching layers, tooling, frameworks, and cloud or on-premises services; productionizing prototypes; and collaborating with engineering, product, machine learning, and security teams. The role emphasizes maintainable, testable code, reliability, monitoring, and long-term platform development.

Build highly available, scalable distributed systems and real-time services supporting Internet-wide scanning, DNS resolution, certificate ingestion, data pipelines, messaging, databases, caching, and APIs. Productionize prototypes, develop reusable tooling and platform capabilities, maintain cloud and on-premise systems, and collaborate with engineering, product, machine learning, and security teams.

Updates machine learning training and test datasets, refines annotation guidelines, initiates model training, and evaluates model performance. The role annotates synthetic text and image data, conducts manual quality analysis, identifies error patterns, reports anomalies, and produces findings on utterance quality, LLM evaluation, ASR bugs, and customer pain points. It also applies basic NLP and linguistic knowledge to ensure data accuracy and consistency.

Leads the design and delivery of scalable data pipelines, dbt models, orchestration workflows, and analytical data models. Establishes data architecture, engineering standards, testing, observability, governance, and quality practices. Partners with stakeholders to deliver data-driven solutions, supports incident response, and provides technical leadership through design reviews, mentorship, hiring, and team development.

Manage technical data for IP Routing hardware NPI programs: support NPI from data entry through factory implementation, maintain product data and Engineering Change Orders across the product lifecycle, and coordinate with engineering, supply chain, and manufacturing to ensure accurate PLM/PDM records and implementation.

Develop and optimize machine learning and statistical models for fraud detection and anti-money laundering. Analyze large datasets to identify fraudulent patterns, build scalable data pipelines and features, evaluate model performance, and collaborate with engineering, product, and operations teams. Communicate insights to technical and non-technical stakeholders while maintaining data privacy and security compliance. The role offers significant ownership in building Oscilar’s early machine learning stack within a remote-first startup.

Analyze complex structured and unstructured datasets, develop predictive models, apply statistical and machine learning methods, identify data quality issues, and generate actionable insights. The role integrates data from multiple sources, supports modernization and governance initiatives, creates dashboards and analytical frameworks, and communicates findings to technical and non-technical stakeholders. It is hybrid in Toronto with onsite work required five days as project needs dictate.

Validates, normalizes, merges, and quality-checks geological datasets including drillhole, geochemical, geophysical, and spatial data. Improves data-processing workflows, supports automation, beta-tests AI/ML tools, and provides geoscience expertise to product and engineering teams. Tracks client projects, professional-services hours, timelines, blockers, and escalations while transforming raw geological information into reliable inputs for software products and AI models.

Design and optimize enterprise data architectures, pipelines, and application workloads using SQL, Spark/PySpark, Python, Apache Iceberg, AWS, ClickHouse, and Aurora PostgreSQL. Build integrations with data virtualization and governance tools, support greenfield projects, optimize databases and Spark jobs, and collaborate on cloud, infrastructure-as-code, and CI/CD initiatives. Experience serving financial services and insurance customers is valuable, while telecommunications knowledge is preferred.

Build and improve scalable full-stack products for higher education, owning initiatives across the software development lifecycle from discovery through release. Collaborate with Product, Design, and Engineering in a cross-functional Scrum team, experiment with AI technologies, resolve bugs, complete tickets, and deliver reliable, tested, user-focused features.

Design and execute product analyses using raw user behavior data, own A/B experiments from design through interpretation, and translate findings into recommendations for product and engineering teams. Build repeatable analytical tooling, apply statistical modeling, and use AI tools to accelerate exploration, analysis, and insight delivery across consumer products, media, gaming, and e-commerce.

Build and operate high-volume telemetry and analytics data pipelines supporting real-time monitoring, product decisions, experimentation, and user experience analysis. Design event instrumentation, schemas, quality checks, deduplication, late-event handling, and identity stitching. Partner with data science, product, and SRE teams to shape requirements, and integrate AI tooling into development and data quality workflows.

Manage interfaces and eligibility files for group insurance: perform daily data loading, validation and updates, document procedures, communicate with clients and stakeholders, and recommend process improvements and automation.

Lead end-to-end predictive analytics and machine learning initiatives, including forecasting, churn prediction, and risk modeling. Translate business needs into scalable ML solutions, build and deploy models on AWS SageMaker, develop production ML pipelines, and oversee monitoring, governance, explainability, and retraining. Collaborate with engineering and MLOps teams, communicate insights to stakeholders, and mentor junior data scientists while providing hands-on technical leadership.

Own the analytical function for a core business area such as growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; generate actionable insights; and influence product, growth, finance, and operations decisions. The role requires independent data work, strong analytical and communication skills, first-principles problem solving, and ownership of ambiguous initiatives through measurable business impact.

Own the analytical function for a core business area such as growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; generate insights; communicate recommendations; and drive decisions and measurable outcomes. The role requires independent data work, first-principles thinking, stakeholder influence, and end-to-end ownership of ambiguous problems.

Own end-to-end data science for growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; generate actionable insights; and influence product, growth, finance, and operations decisions. The role requires independently structuring data, solving ambiguous problems, communicating findings persuasively, and driving measurable business outcomes.

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.

Own end-to-end data science and analytics for a business area such as growth, product, AI and automation, or strategic partnerships. Define business questions and metrics, extract and structure data, conduct analyses and experiments, build reports, communicate insights, and drive data-informed decisions with cross-functional stakeholders. The role requires strong analytical thinking, independent problem ownership, and persuasive communication.