Lead Data Scientist - Financial Crime

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 Lead Data Scientist - Financial Crime Overview:
Within Financial Crime Solutions, we build and deliver products powered by payments data to detect and prevent financial crime. Our teams combine data science with deep expertise in payments to support financial institutions in tackling money laundering and fraud. As a Lead Data Scientist, you will serve as a senior individual contributor responsible for designing, building, and continuously improving machine learning models used in production to detect anomalous behaviour in transaction data.

You will work closely with a Principal Data Scientist and a Director of Data Science, contributing to technical direction while owning delivery and execution in your area. The primary focus is Anti-Money Laundering (AML), with flexibility to support adjacent areas (e.g. fraud, A2A, crypto) depending on team priorities. 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 the development and improvement of AML models focused on anomalous transaction behaviour in card payments.
Own problems end‑to‑end, from problem framing and prototyping to production improvement.
Influence modelling approaches and technical direction in collaboration with senior data science leadership.
Analyse large‑scale payments data to identify patterns linked to illicit activity.
Drive improvements in model performance, stability, explainability, and scalability.
Partner with Engineering and Product teams to ensure effective deployment and maintenance in production.
Produce and maintain clear model documentation, including assumptions, limitations, and performance characteristics.
Contribute to technical standards and best practices.
Ensure all work aligns with regulatory, privacy, and security requirements.
All About You   Strong Python expertise with experience in standard data science libraries and distributed data processing frameworks such as PySpark.
Proven ability to design, deploy, and maintain machine learning models in production.
Experience working with transactional or behavioural data at scale, with strong problem‑solving ability in noisy, high‑dimensional environments.
Hands‑on experience with distributed data platforms such as Databricks and ML lifecycle tools such as MLflow.
Highly autonomous and outcome‑focused, with the ability to drive work independently while aligning with broader technical direction.
Strong communication skills, with the ability to engage effectively across technical and non‑technical stakeholders.
Experience working in collaborative environments, including code reviews and cross‑functional delivery.
Pragmatic mindset focused on impact and reliability.
Bachelor’s degree in Computer Science, Engineering, Data Science, or a related quantitative field, or equivalent practical experience.
Preferred   Experience in AML, fraud, or financial crime analytics.
Familiarity with anomaly detection, behavioural modelling, or graph techniques.
Exposure to model explainability, governance frameworks, or regulatory requirements in financial crime.
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 reasonable and identify the type of accommodation or assistance you are requesting.

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