Top Data Jobs in Singapore

Design and build generative and agentic AI solutions and end-to-end ML applications. Analyze structured and unstructured data, develop ML models, deploy pipelines, dashboards and cloud architectures. Advise clients, run workshops, support pre-sales PoCs, and translate business requirements into scalable AI solutions while collaborating with stakeholders.

Build, deploy, and maintain predictive models and GenAI-powered applications; analyze large datasets to extract insights; collaborate with stakeholders to design solutions and visualizations; architect scalable reusable AI services and stay current with machine learning and generative AI advances.

Lead development and execution of enterprise data governance and strategy. Establish governance frameworks, drive data quality and MDM initiatives, define enterprise data models, collaborate with stakeholders to improve data architecture and accountability, and prepare governance reporting. Explore AI-driven tools to enhance governance.

Design, build and test prototypes using emerging technologies (AI/GenAI, autonomous agents, multimodal AI, quantum). Collaborate with product, design, and engineering teams to iterate, showcase proofs-of-concept, and drive adoption across the bank while researching frontier tech and enabling rapid experimentation.

Build and maintain end-to-end data pipelines and medallion data lake architectures on Databricks and AWS. Implement data governance with Unity Catalog, ensure data quality, prepare analytics-ready datasets, support basic ML pipelines, and collaborate with stakeholders to meet reporting and BI needs.

Lead Group Market Data category and IT software strategic sourcing: establish governance, optimise spend and licensing, drive sourcing strategies, negotiate complex vendor contracts, manage vendor relationships, and partner with Technology, Risk, Finance and business stakeholders to deliver compliant, cost-effective outcomes.

Manage client relationships as a subject matter expert, collaborating with Account Executives to drive revenue, engage clients, and provide product insights for innovative solutions.

Lead transformation of actuarial processes through automation, analytics, and AI. Identify use cases, define requirements, support solutioning, testing and UAT. Manage actuarial data and build reusable analytics pipelines for persistency and claims, improving data quality, lineage and reporting. Support AI pilot delivery and scaling for actuarial reporting and MI.

Lead modernization of actuarial reporting through automation, process redesign, enhanced analytics and AI. Manage PACS/FFP+ projects, define actuarial AI roadmap, deliver pilots and scaled solutions, and coordinate cross-functional stakeholders to improve reporting efficiency, controls, and actionable insights.

Lead the development and deployment of Generative AI applications, collaborate with teams to deliver business use cases, and enhance data science skills.

The Data Scientist will identify new data sets, build processes for data collection, analyze data for potential applications, and research alpha signals for portfolio managers.

The Data Analyst will analyze financial datasets, create data pipelines, engage with vendors, automate data checks, and support investment teams.

The International Sector Analyst will conduct deep research to provide insights on industries, support investment decisions, and collaborate with data scientists and investment professionals.

Abstract and enter clinical data from healthcare records, apply clinical operations policies, troubleshoot VPN/MFA access, maintain confidentiality, and provide guidance to less-experienced team members while ensuring data quality and workflow adherence.

Senior Application Solutions Sales Specialist responsible for driving enterprise application sales, building client relationships, coordinating with technical and delivery teams, and meeting revenue targets. Represents NTT DATA's application and digital services in a hybrid working model.

The Network Technology Sales Specialist engages with clients, identifies opportunities, presents solutions, manages sales processes, and fosters relationships with senior executives to drive growth for NTT DATA.

Embed with strategic customers to design, deploy, and operate large-scale distributed storage and AI infrastructure. Debug performance and networking issues, write code and automation, collaborate with customer engineers, and translate operational requirements into product improvements to ensure successful production deployments.

Lead data science workstreams to build production-ready deep learning and probabilistic models integrated with multi-physics simulations. Own data preprocessing, scalable data pipelines, model architecture, deployment, cross-functional collaboration, customer-facing co-development, internal R&D, and mentoring of junior team members.

Design and deliver end-to-end data architectures and platforms (data lakes, warehouses, lakehouses) for asset management and predictive maintenance. Build ETL/ELT, streaming and API integrations, ensure data governance, security, quality, lineage, and observability, and provide technical leadership to enable analytics and data-driven decision-making.

Lead operational execution of data quality and governance for risk data. Manage data issue lifecycle, perform profiling and root-cause analysis, implement rules and monitoring, support data stewardship, drive BCBS239 attestation, collaborate with tech and risk units, and optimize tools and processes to ensure data fitness and regulatory compliance.

Support EDB Finance by improving and automating incentive administration workflows. Co-design systems and dashboards, review company claims for compliance, perform on-site assessments, prepare reports to senior management, identify policy gaps, and promote incentive governance and risk management.

The Control and Metrics External Data Analyst will manage external data usage, oversee external data initiatives, and support the Chief Data Officer in implementing the Bank's data strategy.

Lead Enterprise AI initiatives by designing, developing, and deploying AI/GenAI solutions. Partner with data engineers, technologists and business stakeholders to prototype, integrate, validate and benchmark LLM and advanced-model applications, manage expectations, and translate analytic results into business outcomes.

This role involves designing and developing AIML/GenAI solutions, collaborating with engineering teams, and translating business needs into AI-driven solutions.

Lead pricing-focused data science for on-demand services: translate business problems into ML solutions, own full ML lifecycle, improve dynamic pricing using econometrics and causal inference, design experiments, monitor models, and communicate results to stakeholders.