Data Scientist

Role & responsibilities 1. Predictive Modelling & Advanced Analytics • Develop predictive models to identify customer targeting opportunities, increase card spending, improve retention and reduce customer churn. • Build and optimize propensity, response, churn, cross-sell and upsell models. • Apply statistical modelling and machine learning techniques to identify customer behaviour and spending patterns. • Perform model validation, performance monitoring and optimization. • Translate model outputs into actionable business recommendations. 2. Customer Segmentation & Portfolio Analytics • Develop customer segmentation strategies for credit and debit card portfolios. • Identify high-value, low-engagement and high-potential customer segments. • Analyze customer spending behaviour, transaction patterns and product usage. • Identify opportunities to improve card activation, utilization, spend growth and customer profitability. • Support customer lifecycle management strategies across acquisition, activation, engagement, retention and attrition. 3. Cards Marketing & Campaign Analytics • Analyze marketing campaign performance across customer segments, products, channels and transaction behaviour. • Develop data-driven customer targeting strategies for card marketing campaigns. • Measure campaign effectiveness, conversion rates, incremental spend and return on investment (ROI). • Design test-and-learn frameworks, including A/B testing, control groups and Champion/Challenger strategies. • Provide recommendations to improve campaign effectiveness, customer response and overall portfolio performance. • Support personalized marketing, offer optimization and next-best-action strategies. 4. Business Intelligence & Data Analysis • Analyze large banking datasets, including customer profiles, card transactions, spending behaviour and campaign history. • Develop analytical dashboards, reports and management presentations. • Identify business trends, portfolio opportunities and potential revenue growth areas. • Monitor key performance indicators and provide insights to senior management. • Present complex analytical findings in a clear and commercially meaningful manner. 5. Business Strategy & Stakeholder Management • Work closely with marketing, product, cards business, risk and technology teams to understand business requirements. • Translate business objectives into analytical use cases and data science solutions. • Provide analytical recommendations to improve customer engagement, card spending and portfolio profitability. • Collaborate with business stakeholders to implement data-driven marketing and portfolio strategies. • Ensure analytical solutions are practical, explainable and aligned with business objectives. 6. Business Knowledge Mandatory • Strong understanding of the credit and debit card business, including card acquisition, issuance, activation, usage, spend growth, retention and attrition. • Knowledge of card revenue drivers, including interchange income, interest income, annual fees, customer profitability and transaction economics. • Understanding of customer lifecycle management, portfolio growth strategies, cross-selling, upselling and customer engagement. • Knowledge of card marketing campaigns, customer targeting, offer optimization, promotional strategies and campaign ROI. • Ability to identify business opportunities through customer behaviour, spending patterns, transaction trends and portfolio performance. • Understanding of card portfolio KPIs, including active cards, spend per card, transaction volume, activation rates, utilization, attrition and customer lifetime value. • Knowledge of banking products, customer segmentation, credit risk, portfolio profitability and risk-adjusted growth strategies. • Ability to translate business requirements into analytical solutions and actionable recommendations that drive revenue growth and improve customer engagement. Preferred candidate profile • Minimum 5+ years of experience in Data Science, Advanced Analytics or Analytics Consulting. • Strong banking experience, particularly in credit card and debit card analytics. • Hands-on expertise in Python, SAS and SQL. • Strong experience in predictive modelling, statistical analysis and machine learning techniques. • Experience working with large banking datasets, including customer profiles, card transactions and campaign history. • Proficiency in customer segmentation, campaign analytics and portfolio analytics. • Understanding of model validation, performance tracking and optimization. • Experience with data visualization tools such as Tableau, Power BI or similar platforms. • Ability to develop analytical reports, dashboards and senior-level presentations. • Experience working with marketing, product, risk and data teams in a banking environment. • Strong analytical, problem-solving and stakeholder-management capabilities. • Experience in card lifecycle analytics, including acquisition, activation, usage, spend growth, retention and attrition. • Familiarity with campaign management platforms, CRM systems or marketing automation tools. • Experience with A/B testing, uplift modelling, next-best-action, offer optimization or customer lifetime value models. • Knowledge of cloud-based data platforms and modern analytics environments. • Experience working directly with banking clients in a consulting or client-facing environment.