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

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 Graph & Relationship‑Based Analytics

• Ability to design and govern an enterprise knowledge layer defining relationship taxonomies, entity resolution rules, and linkage logic
• Ability to translate use cases into relationship‑driven analytical designs, such as Network‑based risk identification, Hidden association and indirect exposure analysis, Related‑party and concentric risk detection
• Strong understanding of how relationship intelligence integrates with rule‑based scoring, ML models, and NLP‑derived entities to enhance decision support systems.

Rule-Based Analytics & Decision Support Systems

• Strong experience defining and governing rule-based scoring models and decision support systems used in financial services domain
• Experience designing hybrid decision architectures, where deterministic rules operate alongside ML and NLP-driven insights.
• Understanding of model overrides, thresholds, explainability, and audit controls required for regulated decisioning systems.

Modern Data Architecture

• Strong experience defining and governing enterprise Data architectures to support batch, micro-batch, and near real-time analytics.
• Ability to translate business, regulatory, and analytics requirements into logical data layering and curation strategies that support reporting, analytics, and AI/NLP use cases
• Working understanding open table formats (e.g. Iceberg, Delta) and their implications on data consistency, auditability, schema evolution, regulatory traceability

Job Requirements:

At least two relevant technical certifications across data platforms, cloud, or analytics technologies.

Data Platforms & Architecture

• Enterprise Data platforms (e.g., Databricks, Snowflake, Azure Fabric, Google BigQuery)
• Open table formats: Apache Iceberg, Delta Lake, Apache Hudi
• Distributed processing & query engines: Spark, Trino/Presto, Hive
• Performance optimization techniques: partitioning, clustering, caching, data skipping, workload isolation
• Cost optimization strategies: tiered storage, lifecycle management, workload governance

Programming & Analytics

• SQL, BTEQ, GCFR
• Python (Pandas, NumPy)
• BI & visualization tools: Power BI, QlikSense

Data Integration & Quality

• Informatica suite: PowerCenter, BDM, IDQ, Enterprise Data Catalogue
• Data ingestion patterns: batch, CDC, streaming
• Data validation, quality controls, and reconciliation frameworks within environments

AI & Automation

• Experience defining transformer-based NLP designs (Data ingestion → preprocessing → modeling → evaluation), ensuring outputs are explainable, traceable, and suitable for regulated environments.
• Strong understanding of AI design governance, including Model output explainability, Confidence thresholds, Human-in-the-loop controls, Regulatory acceptability of AI-assisted insights
• Familiarity with LLM-assisted analytics patterns (e.g. summarization, search, Q&A) and how they integrate into governed enterprise data platforms.

Governance, Risk & Compliance

• Data modelling, critical data elements, regulatory reporting
• Fine-grained data access controls (row-level, column-level, masking)
• Metadata management, lineage, and impact analysis
• Compliance with BCBS 239, MAS, AML/CFT, and internal data standards

Big Data Platforms

• Cloudera Hadoop distribution: Hive, Impala, Spark, Iceberg, Trino

EDUCATION

• Bachelor’s degree in Computer Science, Engineering, or equivalent practical experience

Additional Requirements

Be a Part of the UOB Family

UOB is an equal opportunity employer. UOB does not discriminate on the basis of a candidate's age, race, gender, color, religion, sexual orientation, physical or mental disability, or other non-merit factors. All employment decisions at UOB are based on business needs, job requirements and qualifications. If you require any assistance or accommodations to be made for the recruitment process, please inform us when you submit your online application.

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