Senior Data Scientist

About the role

Design, build, and deploy machine learning models for ad targeting, ranking, and bidding optimization. Develop and scale personalization and recommendation systems using large-scale transaction and behavioral datasets. Lead the design and implementation of incrementality testing frameworks, including lift measurement and causal inference methodologies, to evaluate campaign effectiveness. Build and enhance attribution models, including multi-touch and probabilistic attribution approaches across channels and devices. Partner closely with product, engineering, analytics, and business teams to translate business objectives into scalable data science solutions. Optimize machine learning models for accuracy, performance, latency, and scalability in production environments. Contribute to the architecture, design, and evolution of Mastercard’s AdTech and commerce media products. Drive experimentation strategies, including A/B testing and advanced measurement frameworks.

Responsibilities

• Design, build, and deploy machine learning models for ad targeting, ranking, and bidding optimization.
• Develop and scale personalization and recommendation systems using large-scale transaction and behavioral datasets.
• Lead the design and implementation of incrementality testing frameworks, including lift measurement and causal inference methodologies, to evaluate campaign effectiveness.
• Build and enhance attribution models, including multi-touch and probabilistic attribution approaches across channels and devices.
• Partner closely with product, engineering, analytics, and business teams to translate business objectives into scalable data science solutions.
• Optimize machine learning models for accuracy, performance, latency, and scalability in production environments.
• Contribute to the architecture, design, and evolution of Mastercard’s AdTech and commerce media products.
• Drive experimentation strategies, including A/B testing and advanced measurement frameworks.

Requirements

• Experience in Data Science, Machine Learning, AdTech (Preferred), Marketing Science, or a related field.
• Proven experience building and optimizing ad bidding systems, including RTB optimization, budget pacing, bid shading, and auction-based decisioning.
• Hands-on expertise developing personalization, recommendation, and targeting systems at scale.
• Strong background in incrementality measurement, experimentation, A/B testing, causal inference, and advanced attribution modeling.
• Deep understanding of the digital advertising ecosystem, including DSPs, SSPs, ad exchanges, identity solutions, targeting strategies, and measurement methodologies.
• Proficiency in Python and related data science libraries, with strong experience in data manipulation, feature engineering, and model development.
• Experience working with large-scale distributed data processing frameworks such as Apache Spark.
• Demonstrated success deploying machine learning models into production environments, including batch and real-time pipelines, APIs, monitoring, and model lifecycle management.
• Strong foundation in statistics, machine learning algorithms, optimization techniques, and predictive modeling.
• Experience leveraging cloud platforms such as AWS, Azure, or GCP, along with modern ML infrastructure and MLOps tools.
• Excellent communication and stakeholder management skills, with the ability to explain complex analytical concepts to both technical and non-technical audiences.
• Ability to thrive in a fast-paced, highly collaborative environment and influence technical and business decisions.

Qualifications

• Master's degree in Computer Science, Statistics, Mathematics, Engineering, or a related field.
• Minimum of 7 years of relevant work experience.

Skills

• Python and related data science libraries.
• Large-scale distributed data processing frameworks (e.g., Apache Spark).
• Incrementality measurement, experimentation, A/B testing, causal inference, and advanced attribution modeling.
• Understanding of the digital advertising ecosystem, including DSPs, SSPs, ad exchanges, identity solutions, targeting strategies, and measurement methodologies.
• Experience with cloud platforms (e.g., AWS, Azure, GCP).
• Modern ML infrastructure and MLOps tools.
• Communication and stakeholder management skills.

Benefits

• Insurance (including medical, prescription drug, dental, vision, disability, life insurance).
• Flexible spending account and health savings account.
• Paid leaves (including 16 weeks of new parent leave and up to 20 days of bereavement leave).
• 80 hours of Paid Sick and Safe Time, 25 days of vacation time and 5 personal days, pro-rated based on date of hire.
• 10 annual paid U.S. observed holidays.
• 401k with a best-in-class company match.
• Deferred compensation for eligible roles.
• Fitness reimbursement or on-site fitness facilities.
• Tuition reimbursement.

Pay

• San Francisco, California: $138,000 - $221,000 USD
• New York City, New York: $138,000 - $221,000 USD

Schedule

• Full-time

Corporate Security Responsibility

All activities involving access to Mastercard assets, information, and networks come 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.