Senior Data Scientist

Mastercard • Vancouver, BC

9d • CA$111,000 - CA$160,000

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 are 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, validation, deployment, and monitoring. These systems are built to operate at scale and to be repeatable, resilient, and industrialized so they can support 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. As the ecosystem evolves, new forms of commerce are introducing new types of risk that require novel approaches, creative thinking, and rigorous experimentation. In this role, you will focus on the research and development of a new space: Identifying risks in agentic commerce. This is an emerging domain where autonomous or semi-autonomous agents can initiate or influence transactions, creating new challenges for identity, trust, accountability, and risk modeling. You will help define the problem space, explore new data signals and modeling approaches, and develop scalable intelligence capabilities that can shape how Mastercard identifies and manages risk in this next generation of commerce.

Requirements

• Bachelor’s degree (Master’s or PhD preferred) in Data Science, Statistics, Mathematics, Computer Science, or another quantitative discipline
• Strong independent research skills and resourcefulness, enabling you to find solutions and innovate.
• Strong experience applying machine learning and advanced analytics to solve real-world business problems.
• Strong experience writing clean, modular, and well-documented code following Data Science best practices. Ability to collaborate effectively through code contributions, peer reviews, and shared development workflows to ensure robust, maintainable, and efficient solutions
• Strong programming skills in Python and SQL, with experience processing and analyzing large-scale datasets
• Hands-on familiarity with scalable cloud-based data science platforms such as Databricks and Microsoft Azure
• Experience with distributed data processing frameworks such as Apache Spark
• Strong background in statistical modeling, experimentation, and machine learning evaluation
• Ability to identify appropriate techniques for complex and ambiguous problems and rigorously validate solutions
• Critical thinking and a drive to produce high-quality work, ensuring that all solutions meet rigorous standards
• Demonstrated creativity and critical thinking in emerging or loosely defined problem spaces
• Understanding of Agile methodologies, with the ability to drive iterative delivery and experimentation
• Strong communication skills, including the ability to explain complex findings to technical and non-technical audiences

Nice To Haves

• Experience working with weakly labeled data or limited ground truth (i.e. heuristic approach)
• Exposure to graph analytics, entity relationships, or behavioral modeling
• Familiarity with emerging domains such as agentic systems, automation, or autonomous decisioning
• Experience with model deployment

Responsibilities

• Execute the research and development of machine learning and analytical approaches to identify agents and risks in agentic commerce
• Explore new data signals, behavioral patterns, and entity relationships relevant to agent-driven transaction ecosystems
• Help define problem formulations in a new domain where labels, patterns, and risk taxonomies may still be evolving
• Prototype and develop machine learning solutions using Databricks, Spark, and scalable cloud-based environments
• Drive feature engineering and experimentation across structured and potentially unstructured data sources
• Identify appropriate modeling techniques based on business context, data constraints, and problem structure
• Define and implement rigorous validation frameworks to ensure solutions are reliable, generalizable, and aligned with business objectives
• Collaborate with Product, Engineering, and other Data Science partners in an Agile environment to deliver iterative progress in a rapidly developing area
• Contribute to technical best practices across the team

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