Top Data Jobs in Singapore

Lead analytics and experimentation for payments: design and run frontend experiments, quantify growth opportunities, build measurement frameworks and north-star metrics, translate business problems into data science projects, communicate insights to stakeholders, and mentor junior data scientists while owning cross-functional initiatives.

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.

Lead technical strategy for payments optimization to balance conversion, fraud risk, and cost. Design and implement advanced optimization algorithms, guide experimentation and causal inference, collaborate with product and engineering to deploy ML systems at scale, influence product strategy, and mentor junior data scientists.

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.

Lead architecture of a governed, reusable data, knowledge, and skills platform on Databricks to power analytics, AI agents, and real-time decisioning. Design data, semantic, and skills layers, enforce Governance-as-Code, enable regional data residency, and build agent interfaces and APIs while partnering with Data Science, Product, and Risk teams.

Design and build scalable batch and streaming ETL pipelines and data models, collaborate with stakeholders to translate data needs, ensure data quality and governance, and own end-to-end delivery for Knowledge Platform initiatives in Singapore.

Lead design and delivery of scalable batch and streaming data pipelines for Growth use cases. Improve ingestion, scheduling, and pipeline efficiency; integrate third-party sources; ensure data quality; collaborate with product, engineering, and BI to translate business needs into platform solutions.

Architect and lead the Data, Knowledge, and Skills layers enabling analytics, AI agents, and real-time decision systems. Design governed Databricks-based data models, semantic layers/metric stores, agent interfaces, governance-as-code, and regional data localization to ensure compliance, quality, and consistent metrics across the business.

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 FP&A data engineering: build automated pipelines from payment systems/ERP to cloud warehouses, ensure data integrity, design live dashboards and automated forecasting using SQL, Python, and AI/LLMs, and partner with engineering to embed finance logic across reporting and models.

Build and deploy end-to-end machine learning solutions for payment and fraud risk: translate business needs into ML problems, perform data and feature engineering, train and deploy models, analyze issues, develop ML platforms/tools, and collaborate cross-functionally to deliver business impact.

Intern will perform data analysis and develop predictive models using machine learning for semiconductor manufacturing datasets, contributing to insights and decision-making.

As a Data Science Engineer, you will analyze large datasets, develop predictive models, and work collaboratively to enhance data tools and processes.

Analyze semiconductor manufacturing data to identify yield detractors, build data pipelines, apply statistical and machine learning models for yield improvement, support DOE experiments, and create automated dashboards and reports for engineering stakeholders.

Develop and maintain data pipelines and solutions, optimize data structures, build software components, and integrate AI applications. Requires experience in data engineering and strong technical skills.

This role involves building and maintaining data pipelines, developing ETL processes, optimizing data structures, and utilizing AI for enhanced data analytics.

The Principal Data Scientist will lead the development of digital twin technology integrating advanced simulation and predictive analytics, collaborating with engineers to optimize manufacturing processes.

Lead design and delivery of enterprise-grade factory scheduling, optimization, and simulation solutions. Architect global scheduling systems integrating optimization, discrete-event simulation, and reinforcement learning. Provide data analytics, drive cross-site alignment, benchmark solutions, influence senior leaders, and deploy production systems that improve manufacturing metrics.

The Data Scientist will analyze big data, develop software solutions, identify insights, and enhance products/services through data-driven decisions.

The program develops skills in warehouse operations, focusing on GMP compliance, continuous improvement, SOP updates, and master data management for biopharmaceuticals.

Drive labor productivity through program management, data analysis, automation, and AI methodologies to enhance operational efficiency and reduce labor hours.

Perform data exploration, statistical analysis, and visualization on FOUP (wafer carrier) transportation datasets to identify factors affecting transport time. Engage stakeholders, develop methodology and optimization recommendations, and present findings.

Lead Prudential's AI Lab, driving development of AI solutions, managing sandbox operations, overseeing product quality, mentoring teams, and engaging with external AI talent.

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.