Top Data Jobs in Singapore

Designs and deploys AI solutions including generative AI copilots, recommendation systems, RAG, and agentic AI. Builds predictive and generative models using structured and unstructured data, applies advanced machine learning techniques, develops data visualizations and stories, and partners with cross-functional teams on cloud, MLOps, and real-time data platforms. May manage a team depending on seniority.

Lead delivery of data and digital consulting services: define deliverables, engage senior stakeholders, manage small teams, design scalable data pipelines, implement dashboards and analytics, ensure data quality and governance, and drive digital transformation using cloud, BI, and automation tools.

Build and operate production-grade agentic and generative AI solutions for clients. Responsibilities span solution design, model selection, RAG, deployment, MLOps, monitoring, evaluation, observability, security, governance, incident response, resilience, cost optimization, and auditability. The role partners with data, engineering, security, and client stakeholders, establishes reusable production standards, supports regulated environments, mentors junior engineers, and contributes to the growth of CFGI’s Asia-Pacific Data & AI practice.

Owns solution architecture and technical design for client AI systems, including agentic and generative AI architectures, model and vendor strategy, enterprise integration, security, governance, and audit controls. Leads architecture reviews, guides engineers and data professionals, advises executive stakeholders, contributes to proposals, and establishes reusable standards and reference architectures for a growing APAC Data & AI consulting practice.

Owns regional IT governance, data security, AI tool compliance, privacy policies, access controls, incident management, and client security audits across APAC markets. Establishes group-wide data governance standards, manages enterprise tool licensing and usage monitoring, evaluates AI tool risks, and ensures compliance with data residency, cross-border transfer, and client-specific security requirements.

Leads end-to-end delivery of large, complex M&E data center projects, overseeing execution, budgets, schedules, quality, safety, risk, contracts, claims, disputes, and stakeholder relationships. Provides technical leadership across electrical, ACMV, or fire protection systems; manages client and executive engagement; ensures governance and compliance; and leads multidisciplinary project teams through commissioning and handover.

Develop and implement AI/ML and Generative AI proof-of-concept solutions, including data processing, model training, deployment, experimentation, and algorithm optimization. Collaborate with business, technology, and engineering teams to integrate scalable solutions into existing platforms. Apply deep learning, NLP, and large language model techniques to address business challenges, translate real-world problems into data science solutions, and contribute to technical knowledge sharing.

Manage day-to-day contract administration for data centre projects, oversee claims and dispute resolution, maintain documentation and correspondence, advise project teams on contractual risks/opportunities, support reporting, meetings, project closeout, and liaise with legal and stakeholders to ensure contractual compliance.

Design, build, and operate production-grade batch and streaming data pipelines across AWS and Databricks. Develop lakehouse architectures, data models, quality controls, orchestration, lineage, monitoring, and security practices. Optimize Spark and SQL workloads, establish engineering standards, review designs and code, mentor engineers, and collaborate with data owners, analysts, data scientists, architects, and governance stakeholders. Support reliable, well-governed data products for analytics, reporting, and machine learning.

Develop and validate fraud-risk models for public training subsidies. Responsibilities include recalibrating thresholds, engineering features, evaluating model performance, monitoring data and concept drift, investigating anomalies, assessing emerging fraud schemes, and documenting analytical methods. The role requires collaboration with technical and policy stakeholders, clear communication of trade-offs, and reproducible Python and SQL workflows. Preferred experience includes imbalanced data, delayed labels, dashboards, MLOps, cloud data platforms, graph analytics, and public-sector datasets.

The Data Analyst develops and maintains Power BI dashboards, datasets, data models, SQL and DAX solutions, and data pipelines. The role supports AWS cloud analytics integration, data quality, troubleshooting, governance, documentation, and workspace administration. It coordinates data engineers, translates business requirements into technical deliverables, collaborates with stakeholders, and communicates findings to nontechnical audiences while supporting agile delivery and data privacy compliance.

Leads data migration and analysis projects for KYC and CLM platforms. Gathers requirements, documents business rules and data mappings, analyzes data quality and lineage, defines cleansing and transformation rules, and validates migration results through SIT. Coordinates business, technical, QA, and architecture stakeholders; tracks defects, remediation, risks, and project progress. Supports project planning, client presentations, stakeholder management, and business development.

Lead data migration analysis projects involving source and target data profiling, remediation, quality checks, SQL validation, test case design, SIT and UAT validation, and Power BI dashboard development. Collaborate with business analysts, architects, developers, and QA teams to validate mappings, transformations, defects, and regulatory compliance for KYC and CLM initiatives. Coordinate project teams, maintain documentation, provide recommendations, and support client proposals and business development.

Leads Asia-Pacific data center site origination by securing land, grid capacity, power allocations, water, fiber, and government support. Responsibilities include site due diligence, land and infrastructure negotiations, market entry analysis, investment committee recommendations, pipeline management, and handover to development teams. The role requires strong relationships with utilities, landowners, government agencies, developers, and potentially hyperscale customers, plus regular travel across priority markets.

Provides advanced managed services support across cloud, security, networking, and application infrastructure. Resolves complex incidents, monitors systems, meets SLAs, performs proactive optimization, creates knowledge articles, and supports automation. Engages vendors, contributes to projects and disaster recovery, maintains operational systems, and mentors L2 and junior engineers while ensuring client requirements are met.

Develop and optimize Supabase’s Data API based on PostgREST. Responsibilities include improving the PostgreSQL driver and performance, developing features, fixing bugs, resolving incidents and escalated support issues, improving observability, and debugging integrations across the Supabase platform. The role requires strong Haskell development, PostgreSQL and SQL expertise, Linux debugging, open-source contribution, and technical user support.

Manage daily Data team operations, research and analyze datasets, build internal and external reports (including an annual Startup Ecosystem Ranking), and maintain/improve Python scripts and data infrastructure to deliver actionable insights.

Designs and maintains data pipelines, models, storage solutions, dashboards, and analytics frameworks for rail asset management. Analyzes asset data to identify trends and support decision-making, ensures data quality and accessibility, translates stakeholder needs into technical specifications, and collaborates with LTA teams, rail operators, and manufacturers on implementation, testing, and commissioning.

Develops and deploys AI, machine learning, GenAI, automation, and analytics solutions for Group Risk Management. Partners with business, risk, data engineering, data science, and technology teams to translate business needs into measurable outcomes. Guides model selection, GenAI cost estimation, benefit realization, responsible AI, data governance, and regulatory compliance while promoting an AI-enabled culture.

Lead enterprise-wide Data and AI transformation programmes across banking business and technology domains. Responsibilities include programme governance, delivery planning, executive reporting, data governance, AI implementation, risk and dependency management, budgeting, resource planning, stakeholder engagement, vendor management, change adoption, and benefits realisation.

Sell high-bandwidth IP transit and value-added services to ISPs and internet companies, generate new revenue, prepare technical/commercial proposals, manage accounts, coordinate cross-functional teams, track sales plans, and support global customers with up to 50% travel.

Applies data science, operations research, predictive analytics, forecasting, simulation, and optimization models to air network planning and operational decisions. Operates and supports AI, machine learning, and generative AI solutions in GCP using services such as BigQuery, Vertex AI, and Gemini. Analyzes complex datasets with SQL and Python, develops dashboards and reporting tools, evaluates scenarios, supports process improvement, and communicates recommendations to technical and business stakeholders.

Leads data-driven analysis and transformation initiatives across business units. The role analyzes complex customer and operational datasets, develops models, experiments, simulations, campaigns, and dashboards, and presents actionable insights to stakeholders and senior leadership. It partners with product owners and cross-functional teams to optimize journeys, improve efficiency, support strategic decisions, and maximize data value.

Research emerging trends across technology, consumer behavior, markets, and society; translate findings into strategic insights and opportunities; design and lead rapid experiments and proofs of concept; collaborate with banking and technology teams; engage startups, fintechs, and research institutions; present recommendations to senior stakeholders; and promote innovation practices across DBS.

Supports semiconductor manufacturing equipment through hands-on troubleshooting, data analysis, predictive maintenance, automation, and AI-driven optimization. The role monitors equipment performance, identifies trends, improves reliability and productivity, collaborates on smart manufacturing initiatives, maintains technical documentation, and promotes EHS&S compliance. Normal, swing, day, and night shifts may be required. Candidates without prior experience are welcome and will receive comprehensive training.