Top Data Jobs in Singapore

Lead technical delivery of projects, design kdb+ architectures for time-series data, mentor developers, and drive innovation in kdb+ systems.

As a Lead Data Scientist, you will improve dynamic pricing systems, lead ML projects, collaborate with teams, and analyze data for business impact.

Develop and implement data loss prevention policies, assess data security risks, monitor logs for potential breaches, respond to incidents, collaborate with IT teams, and train employees on data protection best practices.

Design, build, and operationalise large-scale cloud data pipelines (batch and streaming) using Databricks/Snowflake and cloud platforms to support reporting, automated systems and AI/ML teams. Administer cloud data platforms, optimise SQL/storage, implement CI/CD, IaC, containerisation and serverless solutions, and collaborate with clients, partners and internal teams to scale data engineering deployments.

The Visiting Professor will participate in teaching and research, supervise graduate students, and mentor junior faculty at NTU's College of Computing and Data Science.

Lead end-to-end data science initiatives for commodity trading, focusing on palm production, weather modeling, commodity flows, tanker analytics, and market insights. Build scalable databases, dashboards, reports, alerts, and decision-support tools using large datasets. Partner with traders, researchers, technology teams, and leadership to drive projects from ideation through adoption. Mentor junior data scientists, establish best practices, and translate complex analysis into actionable commercial recommendations.

Design and maintain high-throughput pipelines for large-scale unstructured data processing. Build transformation, enrichment, vector embedding, hybrid retrieval, multimedia analysis, and computer vision capabilities. Ensure data quality, reliability, testing, observability, and performance while collaborating with data scientists, frontend engineers, and product managers to support national security analytics.

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.

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.

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.

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.

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.

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.

Lead end-to-end cost management for large-scale data centre and mission-critical projects. Define cost strategies, manage cost planning, estimating, forecasting, contract administration and commercial risk. Act as client lead, mentor cost teams, drive value engineering, controls and governance, and support business development in the data centre sector.

Evaluate data centre investment opportunities, build and maintain financial models, prepare investment memos and presentations, perform market research and due diligence, coordinate external advisors, and provide ad-hoc portfolio and performance analysis to support senior management.

Design, build, and operate backend services and data pipelines to ingest, process, enrich, and deliver large scientific, media, and geospatial datasets. Implement REST APIs, event-driven and streaming systems, and Kubernetes-based production infrastructure to support machine learning and agentic AI applications. Collaborate with scientists and ML engineers to translate requirements into scalable, reliable solutions.