Top Data Science Jobs in Vancouver

Data Science Intern supporting analytics projects through exploratory analysis, data characterization, technology research, machine learning methods, documentation, quality reviews, and stakeholder communication. The role involves working with mentors and customers to understand business problems, develop data-driven solutions, and explain technical findings. This is a full-time, hybrid internship in Richmond, British Columbia, lasting up to eight months.

Leads a team of Product Sales Specialists responsible for product adoption, cross-sell, expansion, pipeline, and revenue outcomes. Coaches consultative selling, manages forecasting and performance, develops scalable sales plays, and partners with Account Executives, Account Managers, Product, Marketing, Operations, Data Science, and Enablement. The role also engages in strategic seller opportunities, improves product expertise across sales teams, and builds repeatable commercial motions for Square’s products.

Own the acquisition product roadmap and engineering priorities to drive durable user growth. Define target audiences and channel mix, build product-driven acquisition mechanics (referrals, growth loops), enable channel effectiveness (discoverability, search/AI readiness, integrations, tracking), test new acquisition tactics, and align product investment with Performance Marketing, SEO, Partnerships, and Brand while measuring long-term subscriber value.

Develop enterprise customers’ automation strategies, identify and deploy high-value AI-enabled use cases, quantify ROI, and drive adoption, renewal, and expansion. Build executive and stakeholder relationships, advise on governance and scalable orchestration, coordinate internal and customer teams, interpret usage data, and create repeatable methodologies and playbooks for Zapier’s enterprise customer success program.

Manages a distributed team of three data scientists while personally leading North American marketing measurement. Responsibilities include campaign and event measurement, churn analysis, funnel effectiveness, AI-driven insight generation, and agentic analytics. The role requires translating complex analysis into actionable recommendations, partnering with marketing teams, coaching across time zones, and establishing sustainable delivery practices.

Leads strategy and execution for large-scale data engineering and analytics programs. Coordinates engineering, data science, product, and business stakeholders; manages roadmaps, dependencies, milestones, risks, reporting, and delivery. Ensures data definitions, transformations, models, and downstream analytics remain accurate and fit for purpose while navigating complex, ambiguous initiatives involving multiple teams and partners.

Supports investment analytics discovery, methodology research, scalable performance and risk analytics, data visualization, business intelligence, workflow improvement, data pipeline development, and AI applications. The intern collaborates with project leads to modernize analytics platforms and improve investment decision-making. Candidates should be pursuing a quantitative degree and demonstrate foundational data skills, analytical problem-solving, collaboration, and interest in asset management and technology.

Owns the strategy and roadmap for Duck Creek’s billing product domain, including invoicing, payments, collections, commissions, disbursements, reconciliation, and automation. Partners with engineering, architecture, data, finance, sales, and customers to deliver configurable, scalable, compliant, and explainable billing capabilities. Leads discovery, prioritization, AI-enabled decision support, governance, adoption, and financial outcomes for insurance billing workflows.

Own production-grade analytics pipelines from design through maintenance, build dbt-based analytical models using Kimball dimensional modeling, and deliver reliable data features. Partner with Product, Engineering, and Data Science teams to translate retail signals into business insights. Establish standards for monitoring, documentation, reproducibility, and code quality. Use AI tools to accelerate development and prototype conversational data experiences for natural-language retail data exploration.

Lead and grow a data science team to build merchant registry and profiling capabilities. Define strategy, drive ML model development and deployment at scale, establish best practices, partner with Product and Engineering, and deliver measurable business outcomes while promoting reusable assets and Agile delivery.

OneSix is seeking motivated professionals for future opportunities in data and AI consultancy. Interested candidates should submit their resumes to stay in touch.

As a Senior/Staff Machine Learning Engineer, you will design scalable data pipelines, implement ML algorithms, and collaborate with engineers and Data Scientists to optimize the digital advertising platform.

As a founding Data Scientist/Machine Learning Engineer, you'll develop AI/ML models, enhance product capability, and drive impactful user outcomes while working closely with product teams.

Leads MAU analytics and data science, managing decision scientists and data scientists while setting quality standards. Owns MAU driver modeling, forecasting, causal analysis, experimentation, and exploratory work supporting growth strategy. Establishes consistent analytical practices, partners with revenue, product, engineering, and data engineering leaders, and embeds the team in roadmap planning. Promotes AI-native workflows through coding agents, automation, and analytical tools while coaching team members into strategic advisors.

The Forward Deployment Engineer will deploy and optimize Generative AI solutions, architect scalable infrastructure, and implement data pipelines across multi-cloud environments while collaborating with cross-functional teams to maximize business value.

The Engineering Leader will oversee software and data teams, ensuring product delivery and operational excellence in robotics and IoT systems.

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.