Data Scientist

About the Role:
The Data Scientist will lead analytic workstreams focused on optimizing the credit card portfolio end‑to‑end. By leveraging large‑scale customer and transaction data, the role converts insights into actionable strategies using advanced analytics and machine learning. This position enables smarter decision‑making across the card lifecycle and drives improvements in payment volume, profitability, and risk performance.

Key Responsibilities:

• Lead analytical initiatives across the credit card lifecycle, including acquisition, activation, usage, retention, payment behavior, fraud, and credit risk
• Develop and implement statistical models and machine learning solutions to uncover growth, efficiency, and risk mitigation opportunities
• Perform customer segmentation, lifecycle analysis, propensity modeling, and uplift modeling to guide business strategies
• Build and evaluate experimentation frameworks using control groups, A/B testing, and causal inference techniques
• Analyze large transaction and customer datasets (issuer and network data) to produce insights
• Prepare datasets for predictive models by processing incomplete or unstructured data
• Improve and streamline code for critical business processes
• Design dashboards using tools like Tableau or Power BI
• Communicate complex analytical results clearly to business and client stakeholders
• Identify opportunities for automation and scalable analytical solutions
• Document methodologies, code, and project details
• Collaborate with Product, Marketing, Risk, Fraud, and Technology to operationalize analytical recommendations
• Support senior‑level reporting through presentations, dashboards, and performance tracking
• Contribute to developing best practices and reusable analytical frameworks

Qualifications:

• Master's degree or higher in a quantitative field (Data Science, Statistics, Computer Science, Mathematics, etc.)
• 5-8+ years of data science or advanced analytics experience, preferably in financial services or retail banking
• Proven experience in credit card portfolio analytics, customer lifecycle modeling, or marketing optimization
• Expertise in analytical and statistical modeling techniques such as regression, clustering, and classification
• Experience working with large datasets using SQL, Hive, Hadoop, Spark, Python, or cloud analytics environments
• Strong foundation in statistical inference, experimental design, causal inference, and time‑series analysis
• Hands‑on exposure to supervised and unsupervised machine learning methods
• Background in fraud, credit risk, authorization, or payment optimization
• Ability to convert analytics into strategic recommendations for business stakeholders
• Strong communication skills, especially with non‑technical audiences
• Experience in consulting or client-facing analytical roles