Lead design, development, deployment, and monitoring of production ML models for merchant risk, fraud, AML, and payment-related use cases. Own feature engineering, model lifecycle, validation, production support, and cross-functional collaboration to deliver high-quality, interpretable, and scalable ML solutions using large-scale transaction and merchant data.
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
Overview:
Services within Mastercard is responsible for acquiring, engaging, and retaining customers by managing fraud and risk, enhancing cybersecurity, and improving the digital payments experience. We provide value-added services and leverage expertise, data-driven insights, and execution.
The Data Science team is responsible for developing advanced AI and machine learning solutions that power critical products across Mastercard's network. This role will support the merchant/acquiring side of the business, working with large-scale transaction data and machine learning models focused on merchant risk, payments, fraud detection, merchant credit risk, and Anti-Money Laundering.
We are seeking a Lead Data Scientist to drive the design, delivery, and success of data science initiatives. This role combines deep technical expertise with end-to-end project ownership, strong production focus, and cross-functional leadership to deliver high-impact, production-ready machine learning solutions.
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:
• Design, build, evaluate, enhance, and monitor machine learning and statistical models focused on merchant risk, fraud detection, merchant credit risk, Anti-Money Laundering, and payment-related risk use cases.• Oversee feature engineering, model training, validation, packaging, production support, and performance monitoring across the full model lifecycle.• Ensure high standards for model quality, robustness, interpretability, documentation, and production reliability.• Lead hands-on model development, experimentation, and technical problem solving using large-scale merchant/acquiring and transaction data.• Partner with product, engineering, development, QA, customer success, and business stakeholders to define problem statements, success metrics, implementation needs, and release readiness.• Collaborate closely with AI/ML engineering and development teams to support production model deployment, scaling, operationalization, and ongoing implementation activities.• Operate independently in a lean, highly collaborative team, driving initiatives end-to-end with limited oversight while balancing speed, quality, and production impact.• Deliver work through typical project cycles of approximately 4-6 weeks from development through delivery.• Communicate insights, recommendations, technical trade-offs, and model performance clearly to both technical and non-technical audiences.
All About You:
• Bachelor's or Accelerated Master's degree in Computer Science, Engineering, Data Science, or a related quantitative field, or equivalent practical experience.• Relevant experience in data science, AI, or machine learning roles, with proven ability to own and deliver data science projects end-to-end.• Strong Python expertise with experience in machine learning model development, standard data science libraries, and distributed data processing frameworks such as PySpark.• Hands-on experience with Databricks or similar distributed data platforms, with the ability to work with large-scale, complex transaction datasets.• Experience with machine learning techniques and tools relevant to fraud and risk modelling, including XGBoost or similar approaches.• Proven ability to design, build, deploy, monitor, maintain, and improve production-ready machine learning models.• Experience working with transactional, merchant, acquiring, or behavioural data at scale, with strong problem-solving and critical thinking skills.• Effective communicator with the ability to influence stakeholders and explain complex technical concepts to technical and non-technical audiences.• Ability to balance hands-on technical work with leadership responsibilities in a lean, collaborative environment.
Preferred:
• Advanced degree in a relevant quantitative field.• Experience in payments, acquiring, transaction processing, merchant risk, AML, fraud, or financial crime analytics.• Familiarity with fraud detection, merchant credit risk, anomaly detection, behavioural modelling, graph techniques, 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 [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:
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.
Pay Ranges
Vancouver, Canada: $127,000 - $203,000 CAD
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
Overview:
Services within Mastercard is responsible for acquiring, engaging, and retaining customers by managing fraud and risk, enhancing cybersecurity, and improving the digital payments experience. We provide value-added services and leverage expertise, data-driven insights, and execution.
The Data Science team is responsible for developing advanced AI and machine learning solutions that power critical products across Mastercard's network. This role will support the merchant/acquiring side of the business, working with large-scale transaction data and machine learning models focused on merchant risk, payments, fraud detection, merchant credit risk, and Anti-Money Laundering.
We are seeking a Lead Data Scientist to drive the design, delivery, and success of data science initiatives. This role combines deep technical expertise with end-to-end project ownership, strong production focus, and cross-functional leadership to deliver high-impact, production-ready machine learning solutions.
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:
• Design, build, evaluate, enhance, and monitor machine learning and statistical models focused on merchant risk, fraud detection, merchant credit risk, Anti-Money Laundering, and payment-related risk use cases.• Oversee feature engineering, model training, validation, packaging, production support, and performance monitoring across the full model lifecycle.• Ensure high standards for model quality, robustness, interpretability, documentation, and production reliability.• Lead hands-on model development, experimentation, and technical problem solving using large-scale merchant/acquiring and transaction data.• Partner with product, engineering, development, QA, customer success, and business stakeholders to define problem statements, success metrics, implementation needs, and release readiness.• Collaborate closely with AI/ML engineering and development teams to support production model deployment, scaling, operationalization, and ongoing implementation activities.• Operate independently in a lean, highly collaborative team, driving initiatives end-to-end with limited oversight while balancing speed, quality, and production impact.• Deliver work through typical project cycles of approximately 4-6 weeks from development through delivery.• Communicate insights, recommendations, technical trade-offs, and model performance clearly to both technical and non-technical audiences.
All About You:
• Bachelor's or Accelerated Master's degree in Computer Science, Engineering, Data Science, or a related quantitative field, or equivalent practical experience.• Relevant experience in data science, AI, or machine learning roles, with proven ability to own and deliver data science projects end-to-end.• Strong Python expertise with experience in machine learning model development, standard data science libraries, and distributed data processing frameworks such as PySpark.• Hands-on experience with Databricks or similar distributed data platforms, with the ability to work with large-scale, complex transaction datasets.• Experience with machine learning techniques and tools relevant to fraud and risk modelling, including XGBoost or similar approaches.• Proven ability to design, build, deploy, monitor, maintain, and improve production-ready machine learning models.• Experience working with transactional, merchant, acquiring, or behavioural data at scale, with strong problem-solving and critical thinking skills.• Effective communicator with the ability to influence stakeholders and explain complex technical concepts to technical and non-technical audiences.• Ability to balance hands-on technical work with leadership responsibilities in a lean, collaborative environment.
Preferred:
• Advanced degree in a relevant quantitative field.• Experience in payments, acquiring, transaction processing, merchant risk, AML, fraud, or financial crime analytics.• Familiarity with fraud detection, merchant credit risk, anomaly detection, behavioural modelling, graph techniques, 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 [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.
Pay Ranges
Vancouver, Canada: $127,000 - $203,000 CAD
Mastercard Vancouver, British Columbia, CAN Office
475 Howe St 20th Floor, Vancouver, BC, Canada, V6C 2B3
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