Top Data Jobs in Singapore

Lead the Realtime Data Platform team to define a 1–3 year technical roadmap, modernize the core platform for petabyte-scale real-time analytics, partner with product teams to enable data-driven features, scale and structure engineering teams, and serve as a trusted technical advisor across Data and AI initiatives.

Design and build scalable batch and streaming ETL pipelines and data models. Partner with engineers and business stakeholders to deliver reliable SSOT data, ensure data quality, governance, and support ML/AI use cases. Own end-to-end pipeline design, monitoring, and ongoing improvements.

Partner with Product and Engineering to analyze large datasets, run experiments, and apply statistical methods to measure product performance, identify growth opportunities, set goals and forecasts, and drive product strategy through data-driven recommendations.

Lead the development of AI and ML solutions for underwriting, manage a team of data scientists, and ensure compliance and governance in initiatives.

Leads commercial due diligence for data centre leasing and investment decisions. Develops total cost of occupancy models, benchmarks developer and landlord proposals, validates budgets and pricing assumptions, assesses commercial risks, supports negotiations, and prepares investment recommendations. The role advises clients across concurrent transactions, maintaining cost intelligence, reporting, audit trails, and evidence-based commercial strategies from site evaluation through lease execution.

Owns independent schedule assurance for data centre opportunities, challenging developer programmes and validating critical paths, sequencing, durations, resource assumptions, procurement lead times, commissioning, energisation and service-date credibility. Conducts schedule risk analysis, maintains programme assumptions and mitigation strategies, leads stakeholder review sessions, supports commercial negotiations, reports recommendations to executives, and transfers validated programme intelligence to delivery teams.

Support data scientists to structure and manage operational manufacturing data, build predictive ML models, develop dashboards/visualizations (Power BI/MS Power Platform), and assist project management and stakeholder communications during a January–June 2027 internship.

Leads North American indirect tax requirements and global tax data governance for an SAP S/4HANA transformation. Oversees U.S. and Canadian tax processes, tax determination, reporting, reconciliations, data standards, tax technology integrations, testing, deployment, audit readiness, and compliance. Partners across Tax, Finance, IT, Master Data, Analytics, Procurement, Supply Chain, and Shared Services to improve tax data quality and establish scalable global operating models.

Supports enterprise Data and AI projects through data preparation, ETL pipeline development, dashboard creation, machine learning model testing, database queries, research, documentation, and data validation. Collaborates with data scientists, AI engineers, architects, consultants, and cross-functional teams while participating in agile ceremonies and innovation initiatives. This is a six-month full-time graduate traineeship for candidates without prior work experience.

Develop user-facing financial AI and equity research algorithms across query understanding, document processing, entity recognition, event detection, retrieval, ranking, and quality evaluation. Build datasets, labeling systems, benchmarks, and production evaluation frameworks. Apply large language models, NLP, machine learning, reinforcement learning, knowledge graphs, and multimodal methods while collaborating with engineers to deploy and continuously optimize financial data and knowledge systems.

Intern will evaluate and improve model governance and management practices for predictive maintenance models. They will collaborate with data scientists and equipment experts to pilot an end-to-end ML management framework, document best practices, analyze data, interpret experimental results, and present actionable recommendations supporting operational efficiency and lean manufacturing.

Build and maintain production machine learning models and AI products that improve player retention and engagement. Responsibilities include recommendation engines, segmentation, propensity scoring, Next Best Action systems, AI agent workflows, and personalization solutions. The role requires translating stakeholder needs into pragmatic ML products, collaborating with analysts, engineers, and product teams, and contributing to model deployment, decisioning capabilities, and the AI product roadmap.

Design and build full-stack, cloud-native enterprise applications that manage corporate data from ingestion through serving. Develop APIs, integrations, data pipelines, CI/CD pipelines, observability, and secure data synchronization across networks. Participate in agile sprints, code reviews, documentation, issue resolution, and technical knowledge sharing. The role may also involve integrating LLM and agentic AI capabilities.

The Business Analyst will manage product development and support, translating business needs into application requirements, focusing on data management and governance, and working closely with technology teams.

Lead the data-quality stream to deliver CDE metadata, governance and audit‑ready artefacts for BCBS239 compliance. Manage a multidisciplinary team, engage senior stakeholders, run governance and change initiatives, and build Power BI monitoring dashboards. Translate data governance and lineage concepts into implementable controls and ensure global adoption across business and functional areas.

Leads the enterprise strategy, roadmap, operating model, and delivery of data products and Data-as-a-Service capabilities. Oversees data product engineering, governance, security, adoption, monetisation, and value measurement across the bank. Establishes a Data Products Centre of Excellence, builds cross-functional teams, enables self-service data consumption, and partners with senior stakeholders, technology, risk, compliance, and governance teams to deliver scalable, controlled, and commercially valuable data capabilities.

Supports electrical engineering design delivery for data center and mission-critical projects across APAC. Responsibilities include defining project objectives, schedules, resources, and deliverables; applying engineering judgment; managing technical risks and quality standards; mentoring team members; supporting client relationships; and contributing to bids, proposals, thought leadership, and regional service delivery.

Leads data-driven industrial optimization projects from requirements gathering through deployment and monitoring. Responsibilities include building scalable data pipelines, integrating industrial data from IoT devices, sensors, test benches, and SCADA systems, performing data quality checks and exploratory analysis, developing machine learning models, creating Power BI and Flask dashboards, supporting digital twin simulations, and developing RAG-based chatbot solutions.

Early-career data scientist role to analyze user and content data, build metrics, design recommendation strategies (recall, ranking, cold-start, diversity), run A/B tests, evaluate experiments, explore LLM/multimodal applications, and improve data pipelines and feature logging.

Lead design and development of scalable data infrastructure and self-service tooling for data engineering teams. Provide technical leadership, implement robust systems (Kubernetes/Terraform/GitOps), ensure quality through testing and maintenance, troubleshoot production issues, and collaborate cross-functionally to deliver reliable data platforms.

Lead design and build of high-performance distributed data infrastructure and AI enablement platforms. Optimize systems internals and JVM performance, implement lakehouse and real-time streaming, develop scalable vector indexing and AI knowledge platform, own SDLC for petabyte-scale systems across global regions, drive engineering best practices, and mentor mid-level engineers.

Partner with Product and Engineering to analyze large datasets, run experiments, build statistical models, define product metrics, forecast outcomes, and deliver insights that drive product strategy and roadmap decisions.

Lead design, build, and operate distributed, large-scale (batch/streaming) data platforms and pipelines. Drive technical decisions, performance optimization, data architecture (lakehouse), tooling standards, third-party integration, and mentor junior engineers to enable company-wide data-driven capabilities.

Lead a Singapore-based team to design, build, and maintain scalable data infrastructure and self-service developer tooling. Implement robust, maintainable systems, ensure quality through testing and troubleshooting, collaborate cross-functionally, and mentor engineers while staying current with emerging technologies.

Develop and deploy predictive models and AI/agentic AI solutions using large-scale manufacturing data. Perform EDA, data extraction/cleansing, feature engineering, and statistical modeling. Collaborate with data engineers, manufacturing engineers, UX, and stakeholders to translate insights into operational improvements that boost yield, quality, efficiency, and business value.