Top Data Jobs in Singapore

Analyze large-scale on-chain data to detect anomalous transactions, develop detection mechanisms and risk monitoring frameworks, apply supervised and unsupervised ML, identify malicious accounts via community-discovery algorithms, and design integrated on-chain/off-chain prevention and control systems.

Ensure accuracy, consistency, and quality of master data across commercial systems. Manage master data lifecycle, enforce data governance and GDPR compliance, implement MDM systems, create Power BI/Excel reports, develop data quality controls, maintain business glossary and data lineage, and collaborate with Sales, IT, and compliance teams.

Ensure accuracy, consistency and quality of commercial master data across systems. Implement data governance and GDPR-compliant data management strategies, lead process improvements, support MDM implementations, create Power BI/Excel reports, maintain data lineage/integration, and collaborate with Sales, IT and compliance to resolve data issues and improve data quality.

Manage physical security for a cluster of data center buildings: develop policies, oversee daily operations and staff, run risk assessments and incident response, manage vendors, budgets, compliance, and security technologies, and deliver regular reporting and training.

Lead design and delivery of secure, scalable enterprise data platforms and pipelines. Provide technical leadership on architecture, ETL/ELT, streaming, data modelling, automation, and reliability. Partner with business and risk stakeholders to enable data-driven information risk monitoring, analytics, reporting, and secure-by-design practices to support governance and regulatory compliance.

Lead end-to-end development and productionization of predictive, prescriptive, and agentic AI analytics across commercial, marketing, finance, and franchise domains. Build scalable ML/LLM solutions, implement RAG and orchestration, partner with stakeholders, and drive adoption and business value through analytics products and MLOps practices.

Lead analytics for quick commerce growth across markets: design causal impact and ROI measurement, build BigQuery/dbt SQL models, produce dashboards and agentic AI solutions, influence senior stakeholders, and mentor junior analysts.

Design, deploy, and maintain highly available Kubernetes clusters and GitOps workflows. Build CI/CD pipelines, implement IaC, monitoring, and SRE practices (SLIs/SLOs, error budgets). Lead incident response and automation to reduce toil while collaborating with development and data teams.

Lead application of data science, machine learning and generative AI to drive SME customer lifecycle outcomes. Translate business problems into models, build and deploy scalable production ML/AI solutions, partner with stakeholders and data engineering, monitor model performance, and promote responsible AI and MLOps best practices.

Lead functional business analysis for enterprise KYC/AML capabilities across Group Technology & Ops. Manage stakeholder relationships, define capabilities, translate business requirements into functional and non-functional requirements, oversee testing and scope control, support deployments and post-go-live knowledge transfer, and lead and develop a team of functional business analysts.

Assist in building and maintaining data ingestion pipelines and storage for AI workflows. Implement deployment, security, and monitoring for applications; collaborate with stakeholders and design teams; test and validate data verification and AI workflows; document solutions and follow best practices while continuously learning new tools.

Support the AI team to build generative AI and computer vision solutions: code in Python, explore and analyze datasets, identify patterns, report findings, and implement models using PyTorch, TensorFlow, scikit-learn, and pandas.

Lead and mentor a logistics analytics team to deliver insights on fleet performance, delivery efficiency, and cost optimization. Shape analytics roadmap, design performance metrics and AI data products, apply decision science and operations research, manage end-to-end analytics execution, and present executive-ready recommendations to drive measurable improvements.

Build and productionize ML systems for personalization, search, and recommendations. Apply statistical and ML models using Python and SQL, collaborate with stakeholders, and measure business impact across cloud environments.

Operate, maintain, and optimize hybrid cloud Data and AI platforms (Cloudera CDP/CML, Hadoop, Spark) across AWS and on-prem. Perform platform upgrades, security patching, container/Kubernetes/OpenShift administration, monitoring, troubleshooting, and automation using Shell/Python. Collaborate with Infrastructure, Security, SRE, and Data/ML teams to ensure availability and scalability of AI/ML and Generative AI services (SageMaker, EMR, Bedrock).

Support the Data Science team to design, develop, deploy, and maintain analytics and ML solutions for manufacturing. Responsibilities include data collection/cleansing, EDA, predictive modeling, stakeholder collaboration, production deployment support, and presenting results to leadership.

Analyze casino operations and product performance, build models and ML solutions for floor optimization, demand forecasting and manpower scheduling, create dashboards and A/B tests, collaborate with Operations, Marketing, BI, and IT to translate analytics into business strategy and operational improvements.

Lead a data insights team to deliver analytics, AI and automation solutions that drive measurable business outcomes. Partner with stakeholders to translate problems into analytical initiatives, manage end-to-end analytics lifecycle, operationalize AI-enabled capabilities, establish KPIs and reporting, and mentor data talent to build organizational analytics maturity.

Lead development and evolution of the Group's AI risk framework, translate policy into practical controls, engage business and tech teams, monitor AI use cases, guide resolution of control gaps, and align frameworks with regulatory expectations and industry developments.

The Associate Director will lead engineering strategy, oversee multiple teams, ensure quality delivery of applications, and promote a high-performing, innovative engineering culture.

Design, build, test and support batch and streaming data pipelines and curated datasets on a modern Lakehouse and AI data platform. Implement data modelling, schema evolution, partitioning and data quality controls. Partner with platform teams and consumers, contribute tooling and standards, and lead delivery for workstreams while supporting junior engineers.

Lead and coordinate health, safety and wellbeing activities for office and data centre construction projects. Monitor compliance, record incidents, perform risk assessments/audits, support legal compliance reviews, train staff, and support global H&S campaigns.

Data Scientist II role focused on building AI/ML models for optimization. Responsibilities include data analysis, model training, and collaboration across teams to implement AI/ML solutions.

Lead advanced analytics and ML projects: perform EDA, feature engineering, statistical modeling, and build/optimize production ML and Generative AI solutions (LLMs, embeddings, RAG). Collaborate with engineering and MLOps to deploy and monitor models, create visualizations and dashboards, translate insights for stakeholders, and mentor junior team members.

Lead a cross-functional AI/ML team to define strategy, design and deploy scalable production AI solutions (Generative AI, NLP, vision, time-series), drive research, ensure code quality and CI/CD, mentor engineers, and collaborate with business partners to deliver impactful decision-science products.