Data Scientist

Who you are

• Bachelor’s or Master’s degree in Data Science, Computer Science, Information Technology, Engineering, Mathematics, Statistics, or similar fields
• Strong work experience in data science, ideally in the payments- or cybersecurity-related industry
• Experienced in building statistical and machine learning models in Python and working with large-scale distributed data processing frameworks, such as Apache Spark, Hadoop and Databricks
• Experienced in data management and data mining with SQL, optimizing performance, building data pipelines, and quantitative analysis
• Self-starter with passion for solving problems
• High energy level; flexible, adaptive and strong collaborator
• Well planned, organized & structured to manage time & work effectively
• Excellent communication skills
• Passion for cybersecurity and profound interest in learning new tools, threat vectors, attack techniques, technologies, and best practices in preventing cybersecurity incidents
• Previous experience with graph database and graph machine learning including graph neural network

What the job involves

• We are seeking a highly skilled and motivated Data Scientist to join our Cyber Analytics team within the Security Solutions Data Science organization. This role is critical to driving advanced analytics initiatives, improving fraud detection capabilities, and supporting strategic decision-making across cybersecurity and payment fraud domains
• Gain subject matter knowledge on web application security, commonly exploited cyber vulnerabilities, and methods of online and payment card fraud including the common points of purchase for compromised cards
• Build, develop, and maintain innovative data-driven analytical solutions, including predictive models and machine learning algorithms, on large volumes of data to support analytics and reporting needs across products, markets, and services
• Competently handle large datasets, sifting for patterns and trends and translating those insights into technical rules and solutions
• Combine cybersecurity and transaction data into new and insightful views of fraud and vulnerability across the Mastercard network
• Collaborate with cross-functional teams including product, engineering, and operations to understand product, usage, and data pipelines as well as delivering scalable solutions
• Translate clients and stakeholder needs into technical analyses and/or solutions. Communicate results and business impacts of analytical insight to senior leadership and stakeholders
• Design metrics to track product usage, model performance and other outcomes of interest, with the aim of increasing engagement and improving the security landscape