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 systemlevel expertise across deterministic, rulebased decisioning, machinelearningassisted analytics, NLP on unstructured data, and realtime data to insight use cases, with focus on design, governance, explainability, and regulatory acceptability

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 Financial Crime Analytics, Finance & Risk 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 fitforpurpose, explainable, and regulatoracceptable.

Knowledge Graph & RelationshipBased 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 relationshipdriven analytical designs, such as Networkbased risk identification, Hidden association and indirect exposure analysis, Relatedparty and concentric risk detection
Strong understanding of how relationship intelligence integrates with rulebased scoring, ML models, and NLPderived 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

Technical Skillsets

Certifications

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

What Will Help You Succeed In This Role

An uncompromising commitment to quality, resilience, and regulatory compliance
Ability to lead the transition from legacy EDW / Hadoop platforms to modern architectures
Strong stakeholder engagement skills across business, technology, vendors, and leadership
Excellent problem-solving and decision-making capabilities
Ability to manage multiple concurrent initiatives under tight timelines
Clear and effective communication of technical concepts to non-technical audiences
Deep understanding of Agile and iterative delivery models
Proven ability to collaborate within globally distributed teams

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.

Apply now and make a Difference

VP1 System Analyst (ML & NLP ), GMET