Data Scientist - KLCC

Key Responsibilities:

• Own analytics workstreams for card portfolio optimization across acquisition, activation, usage, retention, payment success, fraud, and credit risk

• Build and deploy statistical models and machine learning solutions to identify growth, efficiency, and risk-mitigation opportunities

• Conduct customer segmentation, lifecycle modelling, propensity modelling, and uplift analysis to inform portfolio strategies

• Design and evaluate test-and-learn frameworks, including control groups, A/B testing, and causal inference approaches

• Analyze large-scale transaction, customer, and behavioral datasets (e.g. issuer data, network data) to generate insights

• Produce data sets for predictive modeling by parsing and aggregating incomplete, unstructured data sources.

• Enhance and optimize codes for critical business processes.

• Design and develop dashboards using software such as Tableau or Power BI.

• Translate complex analytical findings into clear, actionable recommendations for business and client stakeholders

• Identify opportunities to automate repeatable analysis or build streamlined solutions

• Lead transfer of technical knowledge to facilitate business solution implementation.

• Document all projects, including coding and other necessary documentation.

• Partner closely with Product, Marketing, Risk, Fraud, and Technology teams to operationalize analytics-driven strategies

• Support executive-level storytelling through insightful presentations, dashboards, and performance tracking

• Contribute to capability building by documenting methodologies, best practices, and reusable analytical assets

Qualifications:

• Master’s degree or higher in Data Science, Statistics, Computer Science, Mathematics, or a related quantitative field

• 5–8+ years of experience in data science or advanced analytics, preferably within financial services or retail banking

• Demonstrated experience in credit card portfolio analytics, customer lifecycle modelling, or marketing optimization

• Advanced skills in analytics and statistical modeling (e.g., Regression, Clustering, Classification)

• Experience working with large datasets using SQL, Hive, Hadoop, Spark, Python or cloud-based analytics environments for data manipulation and analysis

• Solid grounding in statistical inference, experimental design, causal inference, and time-series analysis

• Hands-on experience with supervised and unsupervised machine learning techniques

• Exposure to fraud, credit risk, authorization, or payment success optimization

• Experience translating analytics into business strategy and client recommendations

• Strong communication skills with the ability to engage non-technical stakeholders

• Experience in consulting or client-facing analytics roles