Top Data Jobs in Singapore

Develop and maintain the bank’s Data and AI risk management framework. Conduct independent Line 2 reviews, assess data controls and quality processes, identify vulnerabilities, report findings, track remediation, evaluate data risk indicators, and support data governance initiatives while monitoring regulatory requirements and industry practices.

Frames North Asia commercial questions, builds end-to-end analyses using AI, evaluates analytical quality, and delivers decision-ready insights. The role applies statistical and predictive methods to brand competitiveness, customer, launch excellence, sales force effectiveness, and investment insights. It partners with regional stakeholders, communicates recommendations to senior leaders, and develops reusable AI workflows and analytical templates.

Audits and validates labeling annotation data for accuracy, consistency, guideline adherence, and regulatory compliance. Identifies errors and root causes, monitors quality KPIs, leads governance cycles, translates data trends into process improvements, and leads the NC Program. Collaborates with agencies and internal stakeholders across labeling operations.

Build interactive Power BI dashboards, extract and clean data, track operational KPIs, conduct market research, source prospective partners, and support pitch decks and strategic proposals. The intern will translate quantitative insights into executive communications while developing skills in business development, pipeline management, lead generation, stakeholder engagement, and data storytelling.

University students will gain hands-on experience developing technology solutions and applying AI, data science, machine learning, deep learning, GenAI, and MLOps to banking challenges. Responsibilities may include building LLM applications, developing models, implementing responsible AI controls, supporting cloud and DevOps initiatives, testing, deployment, and collaborating with cross-functional teams. The internship requires a minimum 12-week full-time commitment and includes assessments and interviews.

Manages quality, schedule compliance, installation standards, QA/QC documentation, and testing for BMS, BAS, and EPMS scopes across data center projects. Leads FAT, commissioning, and integrated systems testing while coordinating with construction, operations, IT, and network teams. Ensures multiple hyperscale projects meet delivery milestones and standardized quality requirements.

Maintain controlled static and reference data for Treasury platforms and downstream trade-processing activities. Responsibilities include creating and validating records, performing maker-checker controls, investigating exceptions, maintaining audit trails, supporting data-quality reviews, and coordinating with Markets, Operations, Risk, Compliance, Finance, Technology, and external providers. The role also supports projects, testing, process improvements, automation, procedures, training, and business continuity while meeting service levels, operational controls, and regulatory requirements.

Lead Data Scientist developing and maintaining payment-data models to detect fraud, scams, and money laundering across Asia-Pacific. Responsibilities include algorithm research, proof-of-concept development, product prototyping, financial crime model governance, client-facing visualizations, white papers, and regional customer support. The role collaborates with global data science and product teams while building locally tailored capabilities and considering privacy, security, regulation, model accuracy, and performance.

Leads advanced data analysis, develops dashboards, KPIs, and analytical models, and translates business problems into data-driven solutions. Works with complex datasets using Databricks, SQL, Python, BI tools, and cloud data platforms. Partners with engineering and governance teams, promotes analytics standards, mentors analysts, and communicates insights to stakeholders. Supports strategic decisions through descriptive, predictive, and prescriptive analytics.

Build and maintain data pipelines, transformations, curated datasets, dashboards, and analytical tools supporting pharmaceutical manufacturing and process operations. Investigate data-quality issues, translate business needs into reliable outputs, collaborate across scientific, engineering, manufacturing, and digital teams, and support testing, deployment, documentation, user adoption, and continuous improvement in a regulated environment.

Leads complex data engineering and analytics initiatives supporting pharmaceutical manufacturing and technical operations. Designs data pipelines, data flows, curated datasets, and scalable analytics solutions; improves data quality and accessibility; translates business needs into technical requirements; and drives adoption across scientific, manufacturing, engineering, and digital teams. Independently manages workstreams, stakeholders, risks, dependencies, prioritization, and value realization in a regulated environment.

Build and productionize machine learning models for semiconductor quality and manufacturing processes. Develop agentic AI, LLM, retrieval-augmented generation, tool-use, and memory capabilities for predictive quality workflows. Create maintainable analytical pipelines, optimize performance and token efficiency, and support testing, deployment, and monitoring. Communicate analytical findings to technical and non-technical stakeholders while applying AI tools responsibly.

Designs and validates data center network architectures for AI infrastructure, including BGP, EVPN/VXLAN, RoCEv2, QoS, and RDMA fabrics. Builds qualification labs, tests switches, NICs, and network operating systems, investigates performance and interoperability issues, troubleshoots congestion and packet loss, and develops documented standards, automation, and product feedback for customer deployments.

Implement and maintain data governance frameworks, policies, standards, and data quality controls. Support data ownership, stewardship, metadata management, lineage, regulatory compliance, governance forums, stakeholder engagement, training, audits, and enterprise data transformation initiatives for banking clients.

Manages and grows public-sector client accounts through new business, expansion, renewals, and solution-based selling. Owns client satisfaction, opportunities, pipelines, forecasts, governance, and compliance. Builds relationships with clients, vendors, and partners while coordinating internal resources to close sales, identify client needs, drive demand, and increase revenue.

Lead regional data science and machine learning initiatives for financial crime surveillance, including AML networks, transaction monitoring, fraud prevention, money mule detection, and sanctions screening. Develop end-to-end models from problem formulation through deployment, monitoring, and support. Partner with business and technical stakeholders, ensure AI/ML governance and data quality, communicate insights, and mentor junior data scientists.

Manage and expand strategic partnerships across Southeast Asia, Japan, Greater China, and South Korea. Recruit and develop GSIs, SIs, agencies, MSPs, OSPs, and other services partners; create joint business plans; drive co-selling, partner-led demand generation, pipeline, revenue, and market expansion. Build executive relationships, lead business reviews, shape services-led go-to-market initiatives, and collaborate cross-functionally on partner activation and customer adoption. Travel across Asia may be required.

Build and maintain FinOps data pipelines, BigQuery and dbt models, cost attribution systems, forecasting and anomaly detection models, and cost-versus-revenue analytics. Identify and implement AWS and GCP savings opportunities, automate governance with Python, Terraform, and Lambda, and partner with Engineering, Finance, Product, and leadership on cloud costs, forecasting, reporting, and architecture decisions.

Leads end-to-end client delivery for data engineering and AI engagements, converting statements of work into plans, backlogs, milestones, and governance. Manages agile engineering teams, client relationships, scope, budgets, risks, quality, AI evaluation, security and privacy approvals, release readiness, and production handover. The role also supports proposals, delivery playbooks, managed-service transitions, and regulated-sector validation requirements.

Analyze financial and operational data for FP&A, maintain and build dashboards and data models, support budgeting and forecasting, investigate discrepancies, validate outputs, and automate recurring reporting processes. The role requires translating business requirements into reliable analytical logic and communicating insights to technical and non-technical stakeholders.

Lead technology audit engagements focused on enterprise data governance, data quality, AI governance, model validation, access controls, and technology risk. Assess governance frameworks, regulatory compliance, and control effectiveness; develop scalable data and AI audit testing programs; support integrated audits; communicate findings to senior stakeholders; and lead regional audit teams. Promote advanced analytics, automation, and AI/ML tools to improve audit effectiveness. Regional travel of 10%–20% is required.

Implement and maintain data governance frameworks, policies, standards, and data quality controls. Manage data ownership, stewardship, metadata, lineage, and issue remediation processes. Partner with business, technology, risk, and compliance stakeholders, facilitate governance forums and training, and support audits, regulatory initiatives, and enterprise data transformation programs for banking clients.

Loads, validates, and delivers hospitality benchmarking data from corporate submissions, automated feeds, and manual files. The role performs data quality checks, resolves discrepancies with providers, manages client report requests, supports data-feed automation, and coordinates with internal teams on delivery issues. It requires strong quantitative, analytical, communication, and Microsoft Office skills, along with attention to data accuracy, confidentiality, and deadlines. Mandarin, Japanese, hospitality technology, financial literacy, API integrations, and SFTP experience are preferred.

Administers enterprise SharePoint, SQL Server, Exchange, Teams, and Skype for Business environments across on-premises, hybrid, and Microsoft 365 platforms. Responsibilities include patching, upgrades, governance, performance optimization, backup and disaster recovery, high availability, PowerShell automation, monitoring, security compliance, incident resolution, change management, client escalations, and on-call support.

Build AI and agent-based solutions, ML models, full-stack React and Python applications, and production-grade AI platform services. Develop MLOps and CI/CD pipelines across GCP and on-premises Kubernetes, deploy model-serving infrastructure, implement observability and security controls, and create reliable agent guardrails, evaluations, and audit trails. Collaborate with enterprise SMEs, contribute to architecture standards, communicate technical recommendations, and own solutions from design through support.