About the role
Lead the data analytics and decision science strategy for Consumer Banking, driving customer segmentation, lifecycle management, and revenue optimization through advanced analytics, ML/AI models, and cross-functional collaboration.
BankingOnsite
Key Responsibilities
- Define the overall CBGS' data analytics strategic and focus areas
- Working closely with business functions/products and external parties to focus efforts to identify business growth opportunities and drive corresponding solutions to capitalize on the opportunities
- Support Consumer Banking's business performance through analytics driven decision making process across multiple customer segment/product portfolio
- Support key business decision making by delivering relevant and value added strategic and tactical analytics and ensure insights are actioned through campaign/marketing and products
- Leverage on analytics and data science to drive optimal decision-making across customer lifecycle (from NTB to ETB to good ETB/loyal), customer segmentation, products lines and credit lifecycles
- Collaborate with ITD and IT SA for designing, building, and maintaining the infrastructure that supports data storage, processing, and retrieval
- Develop data pipelines that move data from source systems to data warehouses, data lakes that enable data extraction and transformation for predictive or prescriptive modeling
- Assess the effectiveness and accuracy of new data sources and data gathering techniques and make full use of and expand usage of internal / external data and proprietary / open source analytics tools
- Drive the use of decision rules, event-based triggers, statistical models, machine learning and AI techniques for automation of wide range of business decisions and operations for optimized ROI and revenue per business decision
- Lead to develop custom data models and algorithms to apply to data sets via AI/ML
Requirements
- Degree in Engineering, Finance, Mathematics, Statistics or other quantitative fields
- Minimum 10 years of relevant experience in customer data analytics and decision science domain
- Strong analytical skills and good knowledge of the banking industry, especially in consumer business area
- Past leadership or coaching experience to analytics team(s)
- Experienced in scoring, propensity modelling, machine learning and optimization techniques with proven results
- Working knowledge of SAS, Python, MS SQL, R and data visualization tools
- Proficient in SQL, SAS, Python and R Programming
- Adept at business problem statement and solutioning with strong domain knowledge in the banking industry
- Strong analytical and communication skills