Senior Data Scientist

Join Mastercard as a Senior Data Scientist, where you will design, develop, and deploy machine learning models for ad targeting, ranking, and bidding optimization. You will play a key role in advancing Mastercard's commerce media and AdTech capabilities through data-driven personalization, measurement, and optimization solutions. You will collaborate closely with product, engineering, analytics, and business teams to translate business objectives into scalable data science solutions.

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