Top Data Jobs in Ottawa

Design, build, and operate production ML signal systems—ranking, retrieval, recommendations, propensity, and next-best-action—covering feature/candidate generation, serving, experimentation, monitoring, and feedback. Define signal contracts (freshness, provenance, confidence), evaluate long-term impact (trust, fairness, compliance), and partner across product, data, and risk teams to deliver reusable customer-intelligence capabilities.

Design, build, and scale Snowflake ingestion pipelines and ML/LLM systems. Create agentic tooling to generate, test, and maintain pipelines. Collaborate with analysts, data scientists, and product teams to deliver reliable, high-performance data infrastructure and operationalize models for an AI-first e-commerce platform.

Drive technical pre-sales for Microsoft cloud database and analytics solutions via demos, PoCs, hackathons, and architecture workshops. Design secure, scalable data architectures, resolve technical blockers, collaborate with engineering, and evangelize Microsoft Fabric, Azure Databases, Databricks, Purview and EDW migrations to accelerate customer adoption.

Lead design, development, and optimization of enterprise data solutions. Build and maintain data pipelines, warehouses, and analytics platforms. Apply data governance, quality, metadata, and lineage practices. Ensure data security and compliance, monitor platform performance, and collaborate with stakeholders while providing technical guidance and mentorship.

Lead pricing modeling and experimentation to optimize margin, conversion, and retention. Build elasticity and willingness-to-pay models, design A/B and bandit experiments, develop pricing automation (DealBot) and monitoring for AI-driven rate recommendations, own ETL/analysis/visualization, and partner with Finance, Risk, Product, and GTM teams to operationalize pricing intelligence.

In this role, you will define and execute the go-to-market strategy, drive growth, and lead customer success efforts at Reflow, a workplace intelligence platform.

As a Product Manager at Reflow, you will lead product strategy, execution, and delivery while ensuring customer engagement and managing engineering dependencies in an early-stage startup environment.

The Director of Product Management will lead product strategy across infrastructure, software enablement, and customer-facing platforms, enhancing both technical and user experience while managing a team of product managers.

Design and maintain enterprise semantic models and AI-ready data products, build and optimize Power BI datasets and DAX metrics, enforce governance and security, enable self-service and AI-assisted analytics, and collaborate with stakeholders and data engineers to support BI and ML integrations.

Lead AdRise data science initiatives to define experimentation, core KPIs, and measurement. Build production analytics and ML systems for targeting, pricing, and auction performance. Set technical direction, mentor data scientists, partner with Product and Engineering, and establish scalable data foundations and reproducible workflows to drive monetization.

Lead executive interviews, stakeholder workshops, and current-state assessments to identify operational gaps and opportunities. Develop business cases, operating models, governance, KPIs, roadmaps, and practical AI-enabled solutions. Advise on AI strategy, innovation, commercialization, customer experience, and change management. Support transition of recommendations to delivery teams and occasionally participate in business development.

Evaluate and label model-generated and human-written code outputs, debug and assess code/agent trajectories, prompt models for complex data science tasks, review visualizations, and report quality trends to improve model behavior.

Lead advanced analytics projects to build, validate and monitor predictive and descriptive models. Determine technical approaches, evaluate diverse data sources, translate findings for stakeholders, and provide technical leadership and mentorship to analytics teams to support business decisions and risk mitigation.

Lead options analysis and systems definition for an Intelligence Mission Data project: gather and trace business requirements, develop system use cases and specifications, evaluate technical solutions, plan organizational impacts, support integration and testing, and prepare briefings and project documentation.

Design and implement data platform architecture, ETL/ELT pipelines, data mappings/lineage and quality rules. Build orchestration workflows, support data migration and CBM initiatives, deploy and monitor AI/ML models, and collaborate with analysts and stakeholders to deliver production-ready data solutions.

Design, develop, and deploy machine learning and NLP solutions at scale. Work with cross-functional teams to build content classification, representation learning, search, and recommendation models using classical ML, deep learning, and LLMs. Process large datasets with Python, SQL, and Spark, and communicate results to stakeholders with clear documentation.

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.

Design and deploy scalable data science and generative AI solutions: acquire and ETL data from S3/RedShift/Spark, analyze data quality, build/validate models (ML, NLP, networks), deploy models to production, and mentor teams while collaborating with stakeholders and engineering.

Design, develop, test, and support enterprise ETL/ELT data integration and pipeline solutions using Talend and relational databases. Build mappings, transformations, reconciliation, and reusable components; optimize SQL; administer Talend tooling; integrate REST APIs via Azure API Management; monitor and troubleshoot production pipelines; support data quality, governance, metadata, lineage, security, and SDLC processes while collaborating with stakeholders and using AI/automation tools for productivity.

Design, develop, deploy, monitor, and support Talend-based enterprise data integration solutions. Build ETL/ELT pipelines, SQL transformations, API integrations, and Talend administration (TMC, Remote Engine). Troubleshoot production data pipelines, perform data reconciliation, implement data governance and security controls, and collaborate with stakeholders to ensure compliant data movement across cloud and legacy systems.

Build reusable Python libraries and reproducible analysis pipelines for image and experimental data; define data models and provenance; partner with wet‑lab scientists to translate requirements and validate methods; establish testing, validation, versioning, and observability for scientific workflows.

Lead development of digital pathology AI models using whole-slide image data to predict clinical outcomes and morphologic features. Drive dataset selection and annotation, coordinate and mentor junior scientists, partner with bioinformatics, statistics, and clinical experts, produce analyses and visualizations for publication, and manage cross-team project execution to build diagnostic products.

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.

Build and scale GTM contact data lifecycle and automation: acquire, enrich, validate, deduplicate, and govern contact data; design pipelines and automations; enforce data quality standards and SLAs; partner with leadership to prototype AI-driven GTM use cases; maintain documentation, monitoring, and reporting to measure data health and automation impact for frontline sales execution.

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.