VP, System Analyst (Data), Group Technology & Ops

uobgroup; Singapore; On-site; SGD 120,000 - 150,000; Full time

Job summary

1011 United Overseas Bank Ltd is seeking a senior data analytics professional to lead end-to-end delivery of AI-enabled analytics platforms. The role focuses on deterministic decisions, NLP on unstructured data, and governance to ensure regulatory acceptability.

The candidate will design data architectures for data lakes and data warehouses, drive modernization, and collaborate with product and platform teams to deliver scalable solutions across Credit Risk, Customer analytics, and Campaign

Qualifications

• Proven experience delivering large-scale analytics platforms across structured and unstructured data.
• Experience with regulatory analytics, risk data, and decisioning within financial services.
• Ability to translate business requirements into scalable analytics architectures and models.

Responsibilities

• Lead end-to-end data and analytics delivery across the full SDLC.
• Collaborate with stakeholders to translate regulatory requirements into scalable designs.
• Define data quality, governance, and lineage for NLP-derived datasets.
• Ensure explainability and regulator-acceptable AI-driven insights.

Skills

Data analytics

NLP

Machine learning

Regulatory analytics

Data governance

Tools

Python

SQL

Spark

Data Lake

Job description

Company: 1011 United Overseas Bank Ltd

About UOB

United Overseas Bank Limited (UOB) is a leading bank in Asia with a global network of more than 500 branches and offices in 19 countries and territories in Asia Pacific, Europe and North America. In Asia, we operate through our head office in Singapore and banking subsidiaries in China, Indonesia, Malaysia and Thailand, as well as branches and offices. Our history spans more than 80 years. Over this time, we have been guided by our values - Honorable, Enterprising, United and Committed. This means we always strive to do what is right, build for the future, work as one team and pursue long-term success. It is how we work, consistently, be it towards the company, our colleagues or our customers.

Job Description

This role requires strong system‑level expertise across deterministic, rule ‑ based decisioning, machine ‑ learning ‑ assisted analytics, NLP on unstructured data, and real ‑ time "data to insight" use cases , with focus on design, governance, explainability, and regulatory acceptability. And expertise in building Retail data analytics solutions to support targeted at Digital engagement/ Acquisition/ profitability/ AUM/ Sales/ management/ Credit Risk Analytics - Credit Risk Exposure, NPL, Counterparty Risk, Credit Scoring & Decision Support Systems - Behavior Scores, Collection, Customer Segmentation with focus on design, governance and scalability.

The role spans requirements analysis, solution design, testing, implementation, and production support ensuring high-quality, scalable, and compliant data platforms that support advanced analytics and AI initiatives.

You will be responsible for end-to-end delivery of enterprise data and analytics solutions leveraging traditional and modern Data architecture. Experience with Campaign Analytics, Credit Scoring & Decision systems, Retail & Wholesale Datamarts will of advantage.

Key Responsibilities

• Lead end-to-end solution delivery for data and analytics across the full SDLC.
• Analyze business and regulatory requirements, translate them into scalable solution designs & provide estimations.
• Communicate complex technical and architectural concepts to business and senior stakeholders in a clear, simplified manner.
• Review and approve test strategies, functional test cases, and data validation approaches.
• Manage risks and issues related to scope, data quality, regulatory commitments, and delivery timelines.
• Participate in product and platform evaluations (RFPs, PoCs) for data, analytics, and AI tooling.
• Partner with production support team to conduct root cause analysis, resolution, and preventive controls.
• Drive productivity, efficiency & quality improvements across delivery and operational processes.
• Lead innovation and modernization initiatives, including data discovery, cataloguing, governance, and AI enablement.
• Ability to design data architectures supporting NLP and AI-driven analytics , including ingestion, curation, and governance of unstructured data within Data Lake, Data warehouse platforms.
• Strong understanding of AI design governance, including explainability, lineage, confidence thresholds, and regulatory acceptability of AI-assisted insights.

FUNCTIONAL SKILLSETS

Enterprise Data, Analytics & Unstructured Data Enablement

• Proven experience delivering large-scale analytics platforms within financial services spanning structured, semi-structured, and unstructured data
• Strong capability in requirements analysis and functional design for analytics use cases involving Transactional data, Investigator narratives, Case notes and alerts, Policy & Customer communications documents
• Experience defining data quality, governance, lineage, and reconciliation controls for both structured and NLP-derived datasets.
• Ability to support self-service analytics while enforcing data access, usage boundaries, and regulatory controls.

Unstructured Data & NLP-Enabled Analytics

• Ability to define data architectures and data flows that ingest, curate, and govern unstructured and semi-structured data within enterprise data platforms.
• Experience translating business requirements into NLP-enabled analytical use cases , such as Text classification and categorization, Entity & relationship extraction, Risk indicator identification, Summarization of alerts, cases, or documents
• Ability to distinguish between exploratory AI outputs and decision-grade, regulator-consumable datasets, and define appropriate controls for each.

Machine Learning - Applied & Statistical Expertise

• Strong understanding of machine learning concepts and statistical principles, including Classification vs regression use cases, Model accuracy, bias, stability, and drift, Feature relevance and data dependency
• Experience defining model inputs, outputs, validation criteria, and usage boundaries for regulated analytics and decisioning.
• Ability to work with data science teams to ensure ML models are fit‑for‑purpose, explainable, and regulator‑acceptable.

Knowledge