Top Data Jobs in Ottawa

Lead Andela Academy, scaling enterprise learning solutions in AI and Cloud. Build relationships, oversee team delivery, and drive revenue growth with high client satisfaction.

As the Senior Data Scientist for AI & Model Risk, you will lead AI risk assessments, coordinate with stakeholders, evaluate AI system designs, and ensure compliance with risk management principles in financial services.

As a Sr. Data Engineer, you'll design and manage data pipelines, enhance data quality, and leverage AI to streamline operations, driving the success of our AI growth platform for real estate.

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.

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.

Lead the development and execution of the NA digital transformation and data strategy, enhancing capabilities in AI and digital technologies across the business.

As a Senior Business Analyst, you will oversee data migration to Microsoft Fabric, ensuring functional requirements, ETL mappings, and user acceptance testing align with enterprise needs.

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 teams designing, developing, and supporting safety-critical air traffic management software and data systems (CAATS/SMI). Provide people and technical leadership, manage requirements, testing, configuration, budgets, and ensure system safety and operational alignment.

The Senior Data Scientist will build models and analyses, design experiments, integrate datasets, and leverage AI for improved workflows and insights in data science.

Perform systems analysis and options analysis for an Intelligence Mission Data project: gather and translate business requirements into system specifications, develop use cases, evaluate technical solutions, ensure integration with business processes, support testing, and prepare reports, briefings, and meeting artifacts for project stakeholders.

The GTM Lead for Insurance will build and manage enterprise insurance accounts, develop relationships with senior industry leaders, and shape sales strategies for AI solutions.

Lead analytics and operational insights for Customer Operations: define core metrics, build dashboards and forecasts, model churn/renewal risk, enable self-serve reporting, implement AI-enabled automation, and partner cross-functionally to drive retention, expansion, and support efficiency.

Lead and scale Scribd's Data & Analytics organization across Product Analytics, Data Science, and Analytics Engineering. Build operating model, data governance, experimentation frameworks, AI-native workflows, and infrastructure to accelerate insight delivery. Develop director-level leaders, recruit talent, and embed data-driven decision-making at the executive level to drive growth, retention, and portfolio-wide strategy.

Lead AI engineering on the Data Platform: build Databricks Apps deployment paths, construct an AI layer over the Medallion Architecture and semantic layer, enable LLM/NLP systems and agentic workflows, embed AI tooling in the developer lifecycle, enforce guardrails for production safety, define metadata/modeling patterns, and mentor engineers to accelerate AI adoption.

Lead and coordinate client legacy data integration into TELUS Health systems. Oversee conversion tasks, ensure data quality, run workshops, identify gaps, propose process changes, escalate issues, prioritize conversion work, and report progress and risks to project management.

Design, build, and maintain robust terabyte-scale data pipelines and stream-processing applications. Collaborate with product teams, participate in full SDLC, automate tests and deployments, monitor operations, and contribute to technical strategy and continuous improvement. Participate in on-call rotation.

Lead and grow a Data Engineering team to build production data pipelines, medallion datasets, and reusable data products. Drive architecture, engineering standards, design reviews, and cross-functional delivery. Partner with Product, Analytics, Data Science, and Platform to enable governed, reliable data foundations for analytics and AI. Mentor engineers and manage multiple concurrent initiatives to deliver predictable outcomes.

As a Data Engineer, you will build and maintain data systems for drug safety AI tools, ensuring reliable data processing and storage for research teams.

Lead and support analytics lifecycle activities: manage analytic data platforms, govern data access, design and implement reporting/BI, apply advanced quantitative modeling, deliver actionable business insights, contribute to policies, work independently on complex projects and mentor junior colleagues.

As a Lead Data Scientist, you'll design experiments, build predictive models, and lead a team while shaping data-driven strategies to enhance AI deployments.

The Senior Data Engineer will lead the design and development of scalable data infrastructure for analytics and AI capabilities, ensuring compliant data processing systems and improving the data platform.

As a Senior Data Engineer, you'll build and manage data pipelines, ensure data quality, collaborate with teams, and uphold data governance at Mozilla.

Use advanced analytics to monitor collections performance, evaluate third-party agencies, design incentive programs, and develop Power BI dashboards. Partner with operations, strategy, and risk to translate insights into actions, document data flows, and deliver training to non-analytical users to improve collections efficiency and portfolio outcomes.