Top Machine Learning Jobs in Vancouver

Develop AI and big data solutions for real-time fraud and cyber-threat detection. Responsibilities include building data pipelines and feature engineering workflows, processing large-scale datasets, improving model monitoring and drift detection, applying MLOps and automation, and deploying scalable AI capabilities. The role collaborates with data scientists, engineers, and product teams to improve model performance, reliability, and operational efficiency.

Build trusted technical relationships with enterprise customers, troubleshoot Samsara hardware, software, APIs, and AI solutions, and optimize implementations. Serve as the primary technical contact, manage incident communications, identify growth opportunities, maintain account records, and collaborate across stakeholders. The role also supports Technical Account Management team development and requires occasional customer travel plus after-hours or weekend coverage when needed.

Develop data science solutions for payments across Square and Cash App. Build AI-powered tools, ETL pipelines, dashboards, machine learning models, experiments, and cloud data systems. Partner with Product, Engineering, Finance, and Operations to improve payment success, cost efficiency, risk mitigation, and decision-making. Translate business needs into practical solutions, research questions independently, and operationalize data processes and infrastructure.

Owns quality assurance for cryptocurrency and sanctions compliance programs, including sampling, decision reviews, metrics, risk reporting, corrective actions, and continuous improvement. Partners with BSA/CFT and Sanctions program owners and leadership to ensure compliance standards align with regulatory requirements and product growth. Requires deep AML, KYC, sanctions, and fintech compliance expertise, strong analytical and communication skills, executive influence, and AI fluency.

Build and improve Confluence AI experiences across frontend, backend, and AI integrations. Own features from design through launch, apply generative AI, retrieval, agents, and evaluation techniques, create automated quality testing, monitor production performance, run adoption experiments, and collaborate cross-functionally. Mentor engineers and contribute to architecture, observability, testing, safeguards, and scalable software delivery.

Leads AI transformation, governance, quality oversight, risk management, and responsible adoption across Pfizer Global Supply and QSEO. Establishes GxP-compliant AI lifecycle practices, inspection-readiness frameworks, governance forums, portfolio monitoring, inventory management, metrics, and reporting. Advises senior leaders on AI regulations, deployment risks, investment priorities, and compliance while leading cross-functional initiatives and influencing stakeholders across Quality, Digital, Operations, and R&D.

Leads GitLab’s Product Data Science team, setting its strategy, roadmap, analytical standards, and priorities. Partners with Product and Engineering leaders on experimentation, causal inference, analytics, and production modeling. Manages, hires, coaches, and retains data scientists while building a high-performing function. Provides hands-on technical review of models, analyses, and queries, and communicates insights and risks to senior leaders.

The Data Scientist will partner with Sales, Revenue Operations, and Finance to analyze GTM performance, build and productionize machine learning models, conduct experiments, forecast outcomes, and identify growth opportunities. Responsibilities include statistical analysis, causal inference, Python and SQL development, MLOps, agentic workflow automation, semantic-layer contributions, executive reporting, and communicating insights to nontechnical stakeholders in a fully remote environment.

Own company-critical technical problems and long-term strategy across multiple organizations. Architect scalable customer-facing systems and platforms, remain hands-on in software development, influence senior product and engineering leaders, establish engineering standards, and drive responsible AI adoption in products and workflows. The role requires deep expertise in large-scale software systems, consumer products, distributed systems, and production ML and LLM applications.

Own end-to-end analytical problems across risk, fraud, growth, and lending products. Develop strategies, monitor portfolio and macroeconomic trends, build metrics and analytical frameworks with Engineering and Machine Learning teams, and influence product, regulatory, compliance, and launch decisions. Engage external partners, translate complex insights into action, create AI-assisted workflows, and mentor junior analysts.

Lead Block’s global Workforce Optimization function across customer-facing and regulated operations. Establish strategy for forecasting, capacity planning, staffing, real-time management, analytics, automation, and vendor governance. Build predictive workforce systems, align operational and financial plans, manage risk and compliance capacity, oversee incidents and escalations, and develop a multidisciplinary global team supporting employees, outsourced partners, and AI systems.

Leads compliance technology oversight for transaction monitoring, sanctions screening, customer risk scoring, and AI-driven case resolution models. Builds AI-powered automation, governance frameworks, human-in-the-loop controls, real-time monitoring dashboards, data-quality controls, and model performance processes. Partners with engineering, machine learning, policy, regulators, and independent monitors to ensure models and data pipelines are explainable, auditable, exam-ready, and aligned with AML requirements across fiat and virtual currency products.

Build predictive and machine learning models, analyze large datasets, standardize data models, optimize data architectures, and translate findings into actionable business strategies. The role uses Python or R, SQL, relational databases, BigQuery, and statistical modeling to support AI applications, operational efficiency, sales, and profitability.

Build automated reports, dashboards, analytical workflows, benchmarks, and data-driven tools using utility and AMI data. Analyze outage performance, power quality, device performance, reliability, and operational metrics to identify trends and improvement opportunities. Support customer analytics onboarding, data validation, reporting configuration, and ongoing insights. Partner cross-functionally with Product, Engineering, Analytics, and NOC teams while applying Python, SQL, AI, and machine learning techniques.

Develop and scale production machine learning models for credit decisioning, including default prediction and approval decisions. Build training, decisioning, monitoring systems, data pipelines, features, and algorithms. Conduct research, develop prototypes, and collaborate with engineering, credit, and product teams to deliver machine learning products from inception through business impact.

Lead client software projects from planning through delivery, managing scope, schedules, budgets, risks, resources, quality, and stakeholder communication. Apply AI tools to automate project planning, reporting, forecasting, and risk management while validating outputs. Collaborate with product and technical teams, facilitate Agile practices, support client expansion, establish PMO governance, mentor project managers, and deliver AI-focused project management training.

Design, implement, test, deploy, and operate highly available distributed services for Azure Monitor at global scale. Build telemetry collection, routing, storage, and data-access APIs and tools; improve reliability, security, observability, and diagnostics; and integrate machine learning and AI-assisted operational capabilities. Participate in on-call and incident response, collaborate across teams, mentor engineers, lead design reviews, and drive architectural alignment.

Develop and deploy advanced machine learning, LLM, and agentic solutions using structured and unstructured data. Build retrieval, tool-use, orchestration, and evaluation systems that are reliable and safe in production. Collaborate with engineering, product, operations, and data science teams to create ML-driven SaaS features. The role also involves agile development, cross-functional communication, customer engagement, mentoring, and continuous learning.

Own the direction and implementation of Babylist’s personalization domain, including recommendation, search, feed models, custom embeddings, and shared representations. Take ambiguous problems from concept through production, owning architecture, deployment, monitoring, retraining, evaluation, and customer impact. Partner with product, design, and data, establish AI development standards, make cross-team technical decisions, and coach senior engineers while remaining hands-on with complex machine learning systems.

Own the strategy and roadmap for a cloud-native, AI-powered security operations platform. Lead the full product lifecycle, conduct customer and market research, prioritize requirements, define features, and collaborate with engineering, AI, UX, sales, customer success, and services teams. The role focuses on agentic AI, visual AI, workflow automation, and simplifying complex B2B security operations experiences for high-tempo operations centers.

Conduct advanced research on large foundation models and post-training techniques for specialized domains. Develop AI-driven systems, research prototypes, and software tools; collaborate with researchers, engineers, and cross-functional teams; publish findings in leading conferences and journals; and present work at industry events.

Leads a team of ML engineers developing and deploying agentic systems, LLM solutions, traditional machine learning models, retrieval pipelines, tool-calling workflows, and evaluation frameworks. The role guides technical design, code reviews, model and vendor evaluations, proof-of-concept development, cross-functional collaboration, mentorship, and production problem-solving across distributed engineering teams.

Develop and productionize fraud detection machine learning models using large-scale behavioral, device, network, and transaction data. Lead investigations into complex fraud attacks, reconstruct attacker behavior, identify trends, and produce technical reports and customer-facing insights. Collaborate with product, engineering, operations, legal, executives, and customers to improve detection systems and automate analytic workflows.

Leads delivery of large-scale AWS and digital transformation programs, overseeing planning, execution, reporting, risk mitigation, quality, customer outcomes, and continuous improvement. Manages multi-functional teams, develops product and service roadmaps, identifies business opportunities, resolves customer issues, and coaches or recruits team members. Collaborates with engineering and enterprise stakeholders while applying agile methodologies, business analysis, and emerging AI, machine learning, data, and cloud technologies.

Leads advanced analytics for collections operations, using statistical modeling, data mining, machine learning, and reporting to identify trends and improve business performance. Develops benchmarks, predictive models, recommendations, and strategic initiatives; researches tools and industry practices; enhances automated reporting systems; and partners with stakeholders across the bank. Provides consulting advice, communicates insights to senior management, mentors analytics teams, and supports technology and process improvements.