Top Data Jobs in Ottawa

Leads marketing and loyalty analytics strategy, measurement frameworks, forecasting, experimentation, campaign reporting, retention analysis, churn modeling, and segmentation. Partners with Marketing and Finance to establish performance goals and translate complex data into actionable recommendations. Automates reporting using analytics platforms, marketing automation tools, experimentation methods, and AI-enabled workflows while identifying opportunities to grow and retain high-value consumers.

Leads company-wide measurement and causal strategy, defining canonical metrics, semantic data architecture, experimentation standards, and strategic models such as marketing mix, elasticity, incentive sensitivity, and forecasting. Partners with engineering and analytics teams to build scalable, production-ready analytical frameworks, improve scientific rigor, and guide executive decisions. Provides organization-wide technical leadership, develops modeling standards, and applies advanced statistical and causal methods to high-impact business problems.

Own the scientific and product roadmap for Fetch’s monetization data products, including audience creation, expansion, causal measurement, and audience improvement. Lead methodology, evaluate evidence, balance reach, precision, revenue, and cost, and convert bespoke studies into scalable products. Partner with Product, Engineering, Sales, and GTM on pilots, positioning, and customer value. Remain hands-on with SQL, Python, statistical modeling, and causal inference while mentoring scientists and influencing senior stakeholders.

Own audience or measurement products for Fetch’s monetization business, from problem framing through delivery. Build predictive models, datasets, population inferences, and causal measurement studies using receipt, partner, panel, survey, and contextual data. Partner with Product, Engineering, Sales, and GTM to productionize analyses, communicate methods and limitations to customers, and improve products using campaign outcomes, holdouts, and fresh data.

Own the data science strategy, analytical roadmap, and experimentation for ranking and relevance. Partner with engineering, product, commercial, and offer teams to improve advertiser outcomes, user value, retention, and marketplace economics. Design rigorous experiments and observational analyses, quantify incremental business impact, establish KPI frameworks and trade-off guardrails, influence launch and investment decisions, mentor colleagues, and build reusable analytical methods using large-scale data systems.

Owns the roadmap and product strategy for private markets, shareholder, and deals financial data. Responsibilities include identifying content gaps, defining requirements and PRDs, improving data quality and discoverability, collaborating with platform, engineering, design, and go-to-market teams, conducting QA, and driving adoption among financial analysts, investment bankers, and corporate development professionals.

Own the product roadmap for surfacing financial data through search, screening, and agentic capabilities. Define requirements, PRDs, and prioritization; collaborate with engineering, design, data platform, AI/ML, and go-to-market teams; and lead feature development from discovery through launch. Improve content structure, tagging, indexing, ranking, search relevance, screening precision, and agentic retrieval reliability for financial analysts, bankers, and corporate development users.

Supports data extraction, management, quality assurance, cleaning, validation, harmonization, statistical and qualitative analysis, and public health program evaluation. Develops analytic datasets and contributes to technical memoranda, reports, presentations, tables, and figures. Analyzes implementation measures and mixed-methods data, interprets findings, and recommends program improvements. Requires collaboration with government and public health stakeholders, use of Microsoft Office and CDC administrative systems, and the ability to obtain and maintain required customer clearance.

Leads advanced data analysis, develops dashboards, KPIs, and analytical models, and translates business problems into data-driven solutions. Works with complex datasets using Databricks, SQL, Python, BI tools, and cloud data platforms. Partners with engineering and governance teams, promotes analytics standards, mentors analysts, and communicates insights to stakeholders. Supports strategic decisions through descriptive, predictive, and prescriptive analytics.

Design, build, deploy, and optimize scalable AI and machine learning systems using large datasets. Develop predictive models, recommendation systems, personalization engines, forecasting solutions, generative AI applications, and production ML pipelines. Collaborate with data, backend, and product teams to integrate models into applications, while monitoring performance, data quality, drift, reliability, and cost. Establish best practices for evaluation, experimentation, reproducibility, and deployment, and research emerging AI technologies.

Build and maintain FinOps data pipelines, BigQuery and dbt models, cost attribution systems, forecasting and anomaly detection models, and cost-versus-revenue analytics. Identify and implement AWS and GCP savings opportunities, automate governance with Python, Terraform, and Lambda, and partner with Engineering, Finance, Product, and leadership on cloud costs, forecasting, reporting, and architecture decisions.

Develop and enhance Grafana data source plugins integrating third-party services and databases. Support plugin reliability and operations in Grafana Cloud, integrate data sources with agentic workflows, improve engineering processes, assist with customer escalations, and collaborate across the Grafana Division. The role requires strong Golang and TypeScript skills, Kubernetes and microservices experience, robust software development practices, and participation in on-call and SRE operations.

Build and deploy end-to-end statistical and machine learning solutions for clients and internal delivery teams. Responsibilities include scoping ambiguous problems, analyzing operational challenges, prototyping AI workflows, translating messy data and real-world constraints into scalable solutions, iterating with stakeholders, and developing reusable tooling and infrastructure. The role combines hands-on data science with technical consulting and requires communication with technical and non-technical stakeholders.

Lead end-to-end data science and machine learning projects, from scoping ambiguous client problems through production deployment. Build and evaluate statistical and ML models, analyze large datasets, run experiments, collaborate with engineers, create reusable tooling, communicate findings to diverse stakeholders, and mentor data scientists. The role combines hands-on data science with technical consulting and requires occasional customer on-call support.

Design and build scalable backend systems, APIs, data models, event-driven services, experimentation platforms, and personalization systems. Partner with Product, Data Science, Analytics, and Design teams to support customer acquisition and engagement. Establish secure, observable, high-performance engineering practices, mentor junior engineers, and participate in monitoring, on-call, and operational support.

Analyze product usage and telemetry data to identify trends, opportunities, and product improvements. Partner with Product Analytics, Data Science, and product teams to define success metrics and measurement plans. Build dashboards, reports, and self-service analyses using SQL and analytics tools, validate telemetry and data quality, and communicate findings through written analyses, presentations, and recommendations.

Leads North American indirect tax requirements and global tax data governance for an SAP S/4HANA transformation. Oversees U.S. and Canadian tax processes, tax determination, reporting, reconciliations, data standards, tax technology integrations, testing, deployment, audit readiness, and compliance. Partners across Tax, Finance, IT, Master Data, Analytics, Procurement, Supply Chain, and Shared Services to improve tax data quality and establish scalable global operating models.

Conducts clinical data analysis, audit coordination, regulatory reporting, workflow documentation, and corrective action planning for clinical operations. Reviews HEDIS, URAC, and NCQA compliance data, develops performance metrics and reports, supports audits and staffing analysis, researches healthcare regulations, and recommends process improvements. The role also assists with clinical staff training, business requirements, operational documentation, and supervisory coverage.

Leads partner-led and enterprise sales growth, primarily through AWS and Microsoft ecosystems. Builds strategic partnerships, develops enterprise accounts, drives end-to-end sales cycles, expands SMB sales processes, positions cloud and digital engineering capabilities, and manages pipeline forecasting. The role also helps shape sales strategy, organization, and scalable go-to-market processes.

Owns biological knowledge representation through LinkML and Biolink ontology engineering, semantic mappings, metadata harmonization, and schema governance. The role administers agentic curation and MCP tooling, develops LLM-driven extraction and validation workflows, and establishes automated quality controls, provenance, CI/CD, and evaluation standards. This senior individual contributor translates biological research requirements into canonical data models supporting genomics, single-cell, screening, imaging, and prior-knowledge datasets.

Own and scale a multi-cloud data platform across AWS and Azure. Manage Apache Airflow, Kubernetes, Terraform, Helm, ArgoCD, CI/CD, observability, big data infrastructure, governance tooling, and FinOps initiatives. Provide end-to-end architectural and operational ownership, improve developer tooling, define platform roadmaps, manage incidents, enforce engineering standards, and mentor data platform engineers.

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.

Own revenue-focused data science from opportunity sizing through production ML. Investigate and measure product changes, build and operate batch and online ML systems for personalization, experimentation, and automation, and partner cross-functionally to integrate models into user-facing products. Establish monitoring, lineage, reproducibility, and security practices; improve data pipelines; mentor data scientists; and define ML development best practices while using AI tools to accelerate delivery.

Own the strategy, roadmap, and delivery of Coalition’s scanning and cyber-risk data products. Partner with Engineering, Data, Security, Underwriting, Actuarial, Design, and customer teams to improve asset discovery, data quality, accuracy, reliability, scalability, and usability. Define metrics, prioritize initiatives, translate technical challenges into product requirements, guide discovery through launch, and develop APIs and experiences that help stakeholders understand and act on cyber-risk intelligence.

Own and evolve WorkOS’s internal data platform, including ingestion, orchestration, Snowflake, dbt models, data governance, reverse ETL, semantic views, and AI-enabled data workflows. Ensure pipeline reliability, freshness, security, and scalability while partnering with Product, Finance, RevOps, GTM, Engineering, and Security. Build monitoring, runbooks, CI/CD gates, access controls, masking policies, and infrastructure automation across AWS and Kubernetes.