Senior AI Engineer

Mastercard • Vancouver, BC

5d • CA$111,000 - CA$160,000 • Hybrid

About The Position

The Security Solutions Data Science team is responsible for developing Artificial Intelligence (AI) and Machine Learning (ML) models that support Mastercard’s Identity and risk capabilities across digital transactions. These models are production-ready and designed to power key products and decisioning systems across Mastercard’s payment networks. The team also leads the research and development of scalable data science systems spanning the full model lifecycle—from data acquisition and feature engineering, through experimentation and validation, to deployment and monitoring. These systems are built to operate at scale and are designed to be repeatable, resilient, and industrialized, supporting both current business needs and the exploration of emerging problem spaces. Services within Mastercard is responsible for acquiring, engaging, and retaining customers by managing fraud and risk, enhancing cybersecurity, and improving the digital payments experience. Within this mission, the Identity Data Science portfolio plays a significant role in developing intelligence-driven solutions that improve how risk is understood and managed across the merchant lifecycle, including building a unified merchant view and trust profile. You will join a dynamic and innovative team working at petabyte scale with cutting-edge big data platforms and technologies such as Databricks and Apache Spark. Our mission is to streamline and automate the repetitive aspects of data science to accelerate innovation in AI/ML-driven fraud detection. We specialize in building scalable pipelines, robust feature engineering workflows, and shared tooling that enables faster and more reliable model development and deployment.

Requirements

• Proven track record of self-directed learning, demonstrating the ability to acquire new skills and knowledge independently
• Strong independent research skills and resourcefulness, enabling you to find solutions and innovate in data engineering
• Strong understanding of data pipelines and end-to-end ML model development workflows, with exposure to entity-centric data systems
• Experience with Python and SQL, showcasing the ability to write clean, readable, and maintainable code
• Experience with big data technologies (e.g., Spark, distributed compute frameworks)
• Hands-on experience with cloud platforms such as Databricks, AWS, or GCP
• Critical thinking and a drive to produce high-quality work, ensuring all solutions meet rigorous standards
• Strong communication skills, enabling effective collaboration with team members and stakeholders
• Experience collaborating across data science, engineering, and governance teams
• Ability and interest in problem-solving, with a proactive approach to tackling challenges
• Openness to learn and apply new technologies, staying current with industry trends and advancements
• Familiarity with Agile methodologies, with the ability to drive iterative delivery and cross-team collaboration
• Bachelor’s degree in Computer Science, Data Analytics, Mathematics, Software Engineering, or a related field or equivalent practical experience
• Contributions to platform standardization, reusability, and shared tooling across teams

Nice To Haves

• Experience working in hybrid environments (cloud and on-premises)
• Familiarity with ML lifecycle and CI/CD practices for data and ML workflows
• Experience with data governance, lineage, and metadata management
• Exposure to batch, streaming, or real-time data pipelines and production ML monitoring/observability
• Experience supporting high-scale production systems in merchant, fraud, or payment domains
• Understanding of security, compliance, and handling sensitive data
• Experience designing scalable databases and data models such as business registries
• Experience with database updates and maintenance

Responsibilities

• Be an integral part of a creative and innovative team, contributing to collaborative projects and sharing insights to drive engineering and data science excellence
• Work with cutting-edge big data platforms (e.g., Databricks, Apache Spark) at petabyte scale, pushing the boundaries of data processing and model enablement
• Partner closely with Data Science teams to enable seamless R&D, scale models, features, and experimentations into reliable systems
• Support the deployment of model features and model artifacts, ensuring seamless integration into production environments and downstream decisioning systems
• Write clean, testable, and maintainable code, ensuring solutions are robust, efficient, and production-grade
• Design, build, and maintain data pipelines that integrate multiple data sources to support a unified merchant registry and trust profile, enabling richer datasets and unlocking new opportunities for innovation
• Collaborate with data operations and governance teams to move and manage data in compliance with security standards, policies, and regulatory requirements
• Contribute to the design and evolution of scalable, entity-centric data models that support merchant identity resolution and longitudinal profiling
• Automate and maintain data workflows in distributed environments, improving reliability and operational efficiency
• Analyze and optimize ETL/ELT processes to support high-performance data access and model execution
• Implement testing frameworks and monitoring capabilities to ensure production solutions are reliable, observable, and continuously improving
• Support incident response, debugging, and performance tuning of production AI/ML systems

Benefits

• competitive pay based on location, experience and other qualifications for the role
• may be eligible to participate in a discretionary annual incentive program

What This Job Offers

• Job Type: Full-time
• Career Level: Senior