Data Scientist II

Mastercard • Vancouver, BC

2d • CA$91,000 - CA$140,000

About The Position

The Security Solutions Data Science team is responsible for developing Artificial Intelligence (AI) and Machine Learning (ML) models that power Mastercard’s Identity and risk solutions across authentication and authorization use cases. These models are production-ready and designed to support key products and capabilities that help make digital transactions safer, smarter, and more trusted. In addition to building models, the team is responsible for the research and development of scalable end-to-end data science capabilities covering the full lifecycle of model creation—from data extraction and feature engineering to validation, deployment, and monitoring. These capabilities must be designed to scale and to be repeatable, resilient, and industrialized so they can support long-term product growth and evolving business needs. 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 space, the Identity Data Science portfolio plays an important role in advancing intelligence-driven solutions that enable more effective risk assessment and decisioning across the merchant lifecycle. You will join a dynamic and innovative team working at scale with modern big data platforms and technologies. In this role, you will focus on solving merchant risk during onboarding and ongoing monitoring through the research and development of a merchant registry and profiling capability within the Identity Data Science portfolio. This includes helping build the foundational data assets, profiling logic, analytical workflows, and machine learning approaches needed to better understand merchant behavior, relationships, and risk signals over time.

Requirements

• Bachelor’s degree in Data Science, Statistics, Mathematics, Computer Science, or another quantitative discipline such as Engineering, Economics, or Physics
• Experience in applying data science and machine learning to solve real business problems
• Solid foundation in statistics, analytics, and core machine learning concepts
• Experience with Python and SQL; familiarity with tools such as Pandas for data manipulation and analysis
• Exposure to large datasets and interest in scalable data processing; familiarity with Spark is a plus
• Experience with 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
• Ability to identify appropriate analytical techniques and validate solutions through structured evaluation
• Critical thinking and a drive to produce high quality work, ensuring that all solutions meet rigorous standards
• Understanding of Agile methodologies, with the ability to contribute to iterative delivery
• Openness to learn and apply new technologies, staying current with industry trends and advancements
• Good communication skills, enabling effective collaboration with team members and stakeholders
• Self-driven with a collaborative mindset and enthusiasm for learning in a fast-paced, innovative environment

Responsibilities

• Analyze large-scale transaction, merchant, and related entity data to identify patterns, trends, and anomalies
• Support the research and development of a merchant registry and profiling capability to strengthen merchant risk assessment during onboarding and ongoing monitoring
• Contribute to feature engineering, entity resolution, and analytical workflows that improve merchant-level intelligence
• Prototype machine learning and analytical solutions under guidance from senior team members
• Help build and maintain scalable data and model workflows using Databricks and Spark-based environments
• Identify appropriate techniques for different analytical problems and help validate solutions through structured testing, benchmarking, and performance evaluation
• Support model monitoring, benchmarking, and iterative improvement of data science solutions
• Work closely with partners across Data Science, Product, and Engineering in an Agile environment to support iterative delivery and continuous improvement

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: Mid Level