Lead Data Scientist, Mastercard

• Master's degree in statistics or a STEM related field
• Considerable working experience as a data scientist
• Strong experience in building large scale machine learning or real-world deep
• Learning models (CNN, RNN, etc.)
• Extensive experience in using Python and hands on experience building models with LLM, Tensorflow, Keras, PyTorch, MXNet
• Proficient in SQL and familiar with PostgreSQL, Hadoop, Spark
• Familiar with data pipeline technologies and best practices with industry leading platforms such as Apache NiFi, Airflow or equivalent
• Effective in communicating technical content to non-technical audiences

Desirable

• Security Solutions Data Science team is a key differentiator for Mastercard, building cutting-edge products and services that are used across authentication, authorization, and dispute phases throughout the entire Mastercard Payment network
• We work with product teams and engineering teams to develop and implement the ML components in security solutions, and also work with sales teams and finance teams to develop sales strategy for these security solutions
• This is an individual contributor role responsible for piloting Generative AI use cases for sales enablement
• The goal is to automate qualitative research through Generative AI and supplement with Mastercard network intelligence
• So that we can enable targeted sales with on-demand industry trend analysis and customer profiling, and diagnose buyer-specific pain points and provide curated narratives leading to sales
• Collaborate with data scientists, engineers, product managers, and sales to research, design, develop, and evaluate generative AI algorithms to address real-world challenges
• Work with multiple disparate sources of data, storage systems, and building processes and pipelines to provide cohesive datasets for analysis and modeling
• Generate and maintain and optimize data pipelines for model building and model performance evaluation
• Develop, test, and evaluate modern machine learning and GenAI models Create and deliver best practice recommendations, tutorials, sample code, and presentations adapted to technical, business, and executive stakeholders