Lead Data Scientist

Mastercard • Vancouver, BC

3d • CA$127,000 - CA$203,000 • Hybrid

About The Position

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.

Requirements

• 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.

Nice To Haves

• 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.

Responsibilities

• 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.

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: Senior