The Data Science Analyst within Group Retail Risk & Control provides advanced analytics and data science support to enhance business risk management. The role focuses on developing predictive models, dashboards, and insights to support compliance, risk monitoring, and operational efficiency across the Personal Financial Services (PFS) business.
Analytics & Modelling
- Develop predictive and analytical models using advanced methods to capture emerging business risk.
- Act as model owner, implementing models and monitoring their performance over time.
- Perform data mining on structured and unstructured data to identify new risk trends and typologies.
- Lead the end-to-end delivery of analytical solutions, from requirements gathering, proof of concept, implementation, to ongoing maintenance.
Data Visualization & Business Intelligence
- Develop dashboards and visualizations to track risk metrics, model outputs, and operational trends.
- Manage and maintain UOB's business intelligence tools and dashboards, e.g., Qliksense.
- Translate complex technical insights into actionable information for technical and non-technical stakeholders.
Collaboration & Stakeholder Engagement
- Work closely with front-line, compliance, and AFC operation teams to understand business needs and risk typologies.
- Liaise with other modelling teams and external vendors during development and testing of models.
- Communicate findings clearly to support decision-making and process improvement initiatives.
Process Improvement & Automation
- Lead projects to identify inefficiencies in banking operations and implement automation solutions to streamline manual processes.
- Support data management and engineering activities, including preparation of data from AML databases for analytical consumption.
Experience & Qualifications
- Undergraduate degree in Statistics, Mathematics, Business Analytics, Actuarial Science, Financial Engineering, or related quantitative field. MBA or postgraduate degree is a plus.
- 3-5 years of experience in financial services or management consulting, preferably in analytics or risk management.
- Strong analytical skills with experience in data visualization tools (Power BI, Qlik) and analytics tools (Python, R, SAS, SQL).
- Meticulous, self-motivated, and capable of translating data insights into business decisions.
