Jobs Companies Mastercard Director, Data Scientist

About this Director, Data Scientist role at Mastercard

Mastercard · Hybrid · Toronto, Canada

Our Purpose

Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential.

Title and Summary

Director, Data Scientist

Overview:
The Mastercard Security Solutions Data Science team is seeking a Director of Data Science to lead the development and delivery of machine learning models focused on Anti-Money Laundering and crypto risk detection- protecting financial institutions and cardholders across the Americas.

In this role, you will operate as a player-coach, combining hands-on technical contribution with leadership of a small, high-performing team of data scientists. You will own end-to-end model development, drive technical direction, and ensure strong alignment with Product and Engineering partners-playing a pivotal role in strengthening Mastercard's financial crime prevention capabilities across the region.

This is a full-time hybrid position based in Toronto, Canada, with an expectation of at least three days per week in the office.

Role:
• Lead a small team of data scientists, setting clear priorities and ensuring high-quality, outcome-focused delivery.
• Act as a senior practitioner, contributing directly to the design, development, and improvement of AML and crypto risk detection models across the full lifecycle — from data preparation and prototyping through to production performance and iteration.
• Define and evolve the technical roadmap for AML and crypto risk detection solutions.
• Partner closely with Product teams to shape the offeåring, identify opportunities to improve model impact, and provide a clear data science perspective.
• Collaborate with Engineering teams to establish effective ways of working, ensuring clear ownership boundaries and efficient delivery across systems and pipelines.
• Ensure high standards of model quality, documentation, explainability, and governance.

All About You:
• Hands on experience in data science with a strong focus on financial crime, AML, or fraud detection, and a demonstrated ability to lead teams while remaining an active hands-on contributor.
• Strong track record of delivering production machine learning models in complex, large-scale environments, with the ability to operate across the full lifecycle from problem framing through deployment and performance evaluation.
• Strong programming proficiency in Python, with hands-on experience working with large-scale datasets and distributed data platforms.
• Highly autonomous and outcome-focused, with the ability to operate effectively in ambiguous environments and influence across both technical and non-technical stakeholders.
• Strong stakeholder management skills, with proven ability to work effectively across Product and Experience leading engineering teams and translating complex technical work into clear direction and outcomes.
• Exceptional communication skills, with the ability to articulate complex technical concepts to executive and non technical audiences.
• Bachelor’s degree in Computer Science, Engineering, Data Science, or a related quantitative field, or equivalent practical experience.

Preferred:
• Prior experience in payments, financial services, or an adjacent domain.
• Experience with AML detection systems, crypto or emerging payments risk, anomaly detection, behavioral modelling, or model governance.

Mastercard is a merit-based, inclusive, equal opportunity employer that considers applicants without regard to gender, gender identity, sexual orientation, race, ethnicity, disabled or veteran status, or any other characteristic protected by law. We hire the most qualified candidate for the role. In the US or Canada, if you require accommodations or assistance to complete the online application process or during the recruitment process, please contact [email protected] and identify the type of accommodation or assistance you are requesting. Do not include any medical or health information in this email. The Reasonable Accommodations team will respond to your email promptly.

Corporate Security Responsibility


All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and, therefore, it is expected that every person working for, or on behalf of, Mastercard is responsible for information security and must:

  • Abide by Mastercard’s security policies and practices;

  • Ensure the confidentiality and integrity of the information being accessed;

  • Report any suspected information security violation or breach, and

  • Complete all periodic mandatory security trainings in accordance with Mastercard’s guidelines.

In line with Mastercard’s total compensation philosophy and assuming that the job will be performed in Canada, the successful candidate will be offered a competitive pay based on location, experience and other qualifications for the role and may be eligible to participate in a discretionary annual incentive program. This posting reflects one or more current openings on our team.

Pay Ranges

Toronto, Canada: $154,000 - $247,000 CAD

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How this Data Scientist salary compares

This role pays $144,369/yrabove the typical range for Data Scientist roles.

$78,306 median $119,725 $173,423

Typical range $99,929–$144,144/yr, from 25 comparable Data Scientist listings on JobsRadar (pay annualized to USD). See Data Scientist salary insights →

About Mastercard

Everyone wants easier ways to pay; we invent them. Checkout lines are slow; we speed them along. Merchants want more sales; we give them data and insights. People need financial access; we connect them. Corporate purchasing is complicated; we make it simple. Commuters are busy; we speed them on their way. Governments need greater efficiencies; we help create them. Small businesses are virtual; we give them access to a world of buyers. Retailers want to fight fraud; we provide the tools.

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