Top Data Jobs in Singapore

Develop and deploy product-disposition workflows and deviation alert systems using statistical tools and ML/AI to improve yield, reliability, and quality. Build algorithms for data mining, pattern recognition, and wafer/die screening. Collaborate with Fab, design, and quality teams, mentor team members, and drive technical innovation to meet KPIs like quality, cost, cycle time, and scale.

Lead the development and execution of the NA digital transformation and data strategy, enhancing capabilities in AI and digital technologies across the business.

Intern will apply data analytics and AI modeling to wet etch and clean processes, focusing on process optimization and technology readiness, while developing technical skills and working on experiments.

As an intern, apply data analytics and machine learning to analyze semiconductor data, build models, and communicate insights to teams.

As a Senior Data Scientist, you'll define and execute roadmaps, partner with IT for system management, design scheduling logic, develop solutions in C#, Python, and SQL, and communicate strategic recommendations to leadership while tracking KPIs.

Design, implement, and maintain data-driven processes and analytics solutions. Collect, cleanse, automate, and prepare data from disparate sources. Build KPIs and dashboards, apply statistical and machine learning methods, collaborate with stakeholders, perform testing and deployment, and train users to enable data-driven decision making and process improvements.

Manage, maintain, and optimize critical database systems (SQL Server, Oracle). Perform installs, patches, backups, access control, monitoring, performance tuning, incident/change/problem management, on-call support, capacity planning, automation, reporting, and collaborate with global teams and site stakeholders.

Lead architecture, development, and operation of Airwallex's data and AI platforms. Drive company-wide data platform solutions, build scalable multi-region pipelines, enforce governance and security, mentor the team, advocate best practices, and influence technical roadmaps to enable AI/ML-driven business impact.

Lead architecture, development, and operation of Airwallex's data and AI platforms. Drive company-wide data platform strategy, build scalable multi-region data pipelines, promote best practices, mentor engineers, and enable data-driven decision making and AI readiness across the organization.

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 analytics and data design for regulatory and financial crime reporting. Build data foundations, validation frameworks, automated test suites, and snapshot/versioning for reproducible reports. Perform deep-dive analyses for anomalies and audits, shape a scalable reporting and risk analytics platform, and partner with cross-functional teams while mentoring adjacent analysts and data scientists.

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.

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. 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.

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.

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