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Senior Data Scientist

Job in Vancouver, BC, Canada
Listing for: MasterCard
Full Time position
Listed on 2026-06-19
Job specializations:
  • IT/Tech
    Machine Learning/ ML Engineer, AI Engineer (Applied/Software), Data Scientist
Salary/Wage Range or Industry Benchmark: 125000 - 150000 CAD Yearly CAD 125000.00 150000.00 YEAR
Job Description & How to Apply Below

Title and Summary

Senior Data Scientist

Overview

Mastercard powers economies and empowers people in 200+ countries and territories worldwide. 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 decision‑making 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 are 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 introduce 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.

What

will you do?
  • 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.
All About You Essential Skills to be successful
  • 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…
Position Requirements
10+ Years work experience
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