Sepenuh Masa Data Scientist (Card Portfolio Analytics) Jobs, in We+ Asia Kuala Lumpur - Maukerja

We are seeking a Data Scientist to lead analytical workstreams for credit card portfolio optimization. You will translate large-scale transaction and customer data into actionable insights, build and deploy statistical models and machine learning solutions, and drive data-driven strategies across the card lifecycle — including acquisition, activation, usage, retention, and risk optimization.

You will work closely with Product, Marketing, Risk, and Technology teams, design test-and-learn frameworks, and present clear, actionable recommendations to senior stakeholders. If you have a strong background in credit card analytics, customer lifecycle modeling, and client-facing analytics, we want to hear from you.

⇒ Main Responsibilities

• Own analytics workstreams for card portfolio optimisation 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.
• Analyse large-scale transaction, customer, and behavioural 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 operationalise 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 & Experience

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