Head of Decision Management SG

CIMB Singapore; Singapore; On-site; SGD 150,000 - 200,000; Full time

Job summary

A leading banking institution in Singapore is seeking a VP of Advanced Analytics to define and drive analytics strategies within customer-focused areas. The role involves supporting business performance through data-driven decisions, managing analytics pipelines, and leading an analytics team. Essential qualifications include a degree in a quantitative field and at least 10 years of experience in data analytics within the banking industry. This position also requires strong leadership skills and expertise in tools like SQL, Python, and SAS.

Qualifications

• 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 with analytics team(s).

Responsibilities

• Define overall data analytics strategic and focus areas.
• Support business performance through analytics-driven decision-making.
• Drive the use of decision rules for business decisions.

Skills

SQL

SAS

Python

R

Data visualization tools

Analytical skills

Communication skills

Education

Degree in Engineering, Finance, Mathematics, Statistics, or other quantitative fields

Tools

SAS

Python

MS SQL

R

Job description

VP, Advanced Analytics, Analytics & Automation, Group Compliance

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. This includes analysing and enhancing customer segmentation strategies, event-trigger strategies, and channel engagement strategies aimed at acquiring, deepening, or retaining customer relationships.
• Support Consumer Banking’s business performance through analytics‑driven decision‑making processes across multiple customer segment/product portfolios. Deliver relevant and value‑added strategic and tactical analytics and ensure insights are actioned through campaign/marketing and product initiatives.
• Leverage analytics and data science to drive optimal decision‑making across the customer lifecycle (from NTB to ETB to good ETB/loyal), customer segmentation, product lines, and credit lifecycles.

Data Analytics Lifecycle Management – Data to Insights, Action, and Feedback

• Collaborate with ITD and IT SA to design, build, and maintain the infrastructure that supports data storage, processing, and retrieval. Develop data pipelines that move data from source systems to data warehouses and data lakes to enable data extraction and transformation for predictive or prescriptive modeling.
• Assess the effectiveness and accuracy of new data sources and data‑gathering techniques, and fully leverage and expand the use of internal/external data along with proprietary/open‑source analytics tools.

Data Engineering and Platform

• Collaborate with ITD and IT SA to design, build, and maintain the infrastructure that supports data storage, processing, and retrieval. Develop data pipelines that move data from source systems to data warehouses and data lakes for downstream analytics.
• Assess the effectiveness and accuracy of new data sources and data‑gathering techniques, and fully leverage and expand the use of internal/external data and proprietary/open‑source analytics tools.

Data Analytics, Modelling and Business Decisioning

• Drive the use of decision rules, event‑based triggers, statistical models, machine learning and AI techniques for automation of a wide range of business decisions and operations, optimizing ROI and revenue per decision.
• Lead the development of custom data models and algorithms to apply to data sets via AI/ML, ensuring the creation of testing frameworks to assess model quality and accuracy.
• Deliver consumer insights to business units such as products, marketing, customer relationship management, and credit operations.
• Lead business simulations and what‑if analyses based on business inputs and market sentiments.

Collaboration & People Management

• Champion projects that acquire, host, process, and deploy data flows and models with clear business objectives, fostering a data‑driven decision‑making culture.
• Coordinate key initiatives and manage business relationships with partners such as Risk Management, Finance, IT, regional counterparties, Credit Bureau, and vendors.
• Provide guidance to team members on analytic approaches, processes, tools, and problem solving.
• Supervise the team to manage end‑to‑end campaign data execution, customer segmentation, CRM, Credit Bureau, and MIS solutions for the Consumer Banking business, ensuring compliance with bank policies, data management standards, and regulatory requirements.

Qualifications

• Degree in Engineering, Finance, Mathematics, Statistics, or other quantitative fields.

Relevant Work Experience

• 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 with analytics team(s).
• Experienced in scoring, propensity modeling, machine learning, and optimization techniques with proven results.
• Working knowledge of SAS, Python, MS SQL, R, and data visualization tools.

Competencies/Skills

• Proficient in SQL, SAS, Python, and R programming.
• Adept at framing business problem statements and solutioning with strong domain knowledge in the banking industry.
• Strong analytical and communication skills.

Seniority level

• Executive

Employment type

• Full‑time

Job function

• Other

Industry

• Banking