Lead Data Scientist, Mastercard

• Demonstrated ability independent contributing to overall team objectives
• Data engineering and data science experience
• Knowledge of model optimization techniques and the ability to work closely with more senior data scientists implementing and optimizing models within big data pipelines
• Experience with XGBoost and holds strong grasp over data science and machine learning concepts
• Experience with SQL language and one or multiple of the following technologies: PySpark, Hadoop, Databricks
• Good knowledge of Linux / Bash environment
• Python, Pyspark
• Good communication skills
• Highly skilled problem solver
• Exhibits a high degree of initiative
• At least an undergraduate degree in CS, or a STEM related field

Desirable

• The Security Solutions Data Science team is responsible for creating Artificial Intelligence (AI) and Machine Learning (ML) models backing its flagship product
• The models generated are production ready and created to back specific products in Mastercard’s authentication and authorization networks
• The Data Science team is also responsible for developing automated processes for creating models covering all modeling steps, from data extraction up to delivery
• In addition, the processes must be designed to scale, to be repeatable, resilient, and industrialized
• You will be joining a team of Data Scientists and engineers working on innovative AI and ML fraud detection
• Work closely with the business owners to understand business requirements, performance metrics regarding data quality and model performance of customer facing products
• Lead the development of advancing fraud detection models specifically credit and debit card transaction level models
• Lead the efforts to enhance the best modeling practices that maintains the competitiveness of our fraud detection models
• Oversee implementation of data and model development pipelines
• Explore fraudulent patterns or trends for feature discovery and enhance fraud detection model performance
• Manage the testing of trained models to ensure their robustness and assess their readiness for deployment