Top Data Jobs in Singapore

Serve as primary customer contact overseeing project delivery and ongoing service operations for data center projects. Manage SLAs, costs, quality, schedules, risks, cross-functional coordination, and continuous improvement to ensure customer satisfaction and contract compliance.

As a Senior Data Scientist, you'll design datasets, conduct model pre-training, and collaborate with teams to develop LLM features for diverse users.

As a Lead Data Scientist, you will design and deploy search and recommendation algorithms, optimizing user experience for GoFood and GoPay systems, leveraging large-scale data and machine learning expertise.

Lead Data Scientist responsible for developing and deploying multilingual large language models, building datasets, and improving AI safety alignment.

As a Senior Data Scientist, you will develop and deploy advanced AI models in deep learning, focusing on computer vision, voice, and text processing. Responsibilities include model optimization, performance evaluation, and collaboration with engineering teams.

The Lead Data Scientist will develop multilingual AI models, focusing on dataset creation and model training, while collaborating with product and engineering teams for deployment.

Lead AML data science and analytics initiatives: perform strategic data analysis, statistical modeling on large datasets, consult with business stakeholders, drive system changes from requirements to implementation, improve data quality, and mentor junior staff while managing risk and compliance.

Leads architecture and development of large-scale batch and real-time data platforms and pipelines. Ensures data quality, governance, reliability, observability, scalability, and cost efficiency while building self-service capabilities. Supports analytics, machine learning, and generative AI teams with trusted data infrastructure. Establishes engineering standards, mentors engineers, and drives platform health across multiple teams. The role requires expertise in data integration, distributed processing, data modeling, cloud platforms, CI/CD, and infrastructure automation.

Leads technical engineering development for large-scale data centre construction projects. Responsibilities include coordinating internal teams, clients, vendors, contractors, and third parties; reviewing designs and proposals; managing risks, issues, quality checks, commissioning, defect rectification, documentation, and handover. The role also prepares progress reports, tracks mitigation actions, and recommends improvements to engineering systems related to technology, cost, schedule, and operations.

Develop and maintain Xceptor-based data automation and integration solutions for banking and capital markets. Responsibilities include configuring MessageHub channels, building Mail and REST/SOAP API integrations, transforming and validating financial data, automating reconciliation and workflow rules, supporting trade lifecycle processes, and troubleshooting production workflows. The role collaborates with architects and business SMEs to deliver scalable integrations with financial platforms and reporting systems.

Designs, develops, and deploys Power Platform applications, automated workflows, RPA desktop flows, Dataverse solutions, and Copilot Studio agents. Integrates solutions with Microsoft 365, Dynamics 365, SharePoint, Azure, and enterprise AI services. The role supports proof-of-concept engagements, ALM, governance, security, compliance, technical documentation, knowledge transfer, client delivery, and post-launch stabilization.

Manage procurement, negotiation, administration, and performance monitoring of data center maintenance contracts. Responsibilities include vendor selection, KPI development, relationship management, dispute resolution, performance reviews, reporting, market analysis, and process improvement across Southeast Asia.

Processes and manages customer records and information as part of Iron Mountain’s operations services. The role supports data handling, digital transformation, records management, data privacy, and related operational workflows. The posting emphasizes opportunities for career development and contributing to sustainable solutions, but provides no specific duties, qualifications, technologies, salary, travel, or clearance requirements.

Lead high-complexity data science initiatives improving search ranking, retrieval, relevance, and evaluation. Develop and deploy production machine-learning and NLP solutions, investigate LLM-based ranking features, optimize SQL queries, and work with sparse privacy-preserving data. Define strategies for ambiguous problems, guide other data scientists, collaborate with product teams, and own projects from scoping through production.

Designs and maintains conceptual, logical, physical, analytical, and dimensional data models supporting reporting, analytics, AI, and business decisions. Develops source-to-target mappings, transformation logic, data dictionaries, and integrated data assets. Profiles data sources, creates analytics tables, supports ingestion and transformation processes, and contributes to data quality, metadata, lineage, and governance. Collaborates with analysts, technology teams, and data platform teams to deliver scalable, reusable enterprise data solutions.

Own NVIDIA’s strategic alliance across APJ, serving as the primary relationship leader and defining regional engagement strategy. Execute co-selling, campaigns, executive briefings, customer programs, and events to generate qualified pipeline, influence deals, and grow partner-sourced revenue. Adapt engagement models across Japan, Korea, India, Southeast Asia, and ANZ while collaborating with sales, marketing, solutions architecture, product, and channel teams. The role requires strong AI infrastructure expertise, independent execution, and frequent APJ travel.

Support autonomous-vehicle data analysis by mining vehicle logs for scenarios and edge cases, building ETL pipelines, developing internal dashboards, and exploring AI agents for automated analysis and scenario generalization. The internship requires Python, Linux, SQL, and database knowledge, with bonus experience in web development, frontend tools, visualization, LLMs, Git, or Docker. Students must commit to a four- to six-month internship.

Design, validate, and deploy AI algorithms for digital health projects. Lead technical innovation, support data platform development and retrospective analysis, collaborate with clinicians and biostatisticians, publish findings, prepare technical reports, and mentor junior AI researchers.

Lead company-wide performance intelligence: build central dashboards and KPI frameworks, produce executive reports, run deep metric analyses (P&L, funnels, retention), create forecasts and ROI models, and design self-serve data infrastructure and automation to guide strategic decisions and resource allocation.

Design, build, and optimize offline and real-time data pipelines and warehouses for product data. Lead end-to-end R&D for online product data pipelines, ensure timeliness and accuracy, perform Flink-based real-time processing and performance tuning, and enable business growth through data products and services.

Design, build, and maintain analytics-ready data marts using dimensional modeling; partner with analytics and business teams to deliver documented datasets; implement resilient batch and real-time data pipelines; architect cloud data infrastructure; enforce data standards, validation, and monitoring to ensure data integrity and scalability.

Build and maintain the risk-control data warehouse, develop thematic metrics and indicators, design offline and real-time data architectures, optimize data pipelines, and deliver reporting and API interfaces for the data mart to support risk control business lines.

Lead development and deployment of GenAI, RAG, ML and Agentic AI solutions for continuous audit, risk assessment, testing and reporting. Collaborate with auditors, data engineers and cross-functional teams to design, evaluate, and scale production-grade models and MLOps on cloud infrastructure. Introduce innovative analytics to improve audit effectiveness and coach stakeholders.

Design, develop, and maintain data infrastructure microservices using C++, optimize for performance, debug issues, and collaborate with teams.

Take end-to-end ownership of backend features and SaaS services in a microservices architecture. Design, implement, deploy, and operate scalable, reliable Node.js services. Work with Redis Streams, MongoDB, Kubernetes, and ensure test coverage, performance tuning, and collaboration with cross-functional teams.