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.