Top Data Jobs in Singapore

Lead commercialization and regional sales for Mastercard Advanced Analytics to financial institutions across Asia Pacific. Own sales strategy, build multimillion-dollar pipelines, manage complex enterprise deals, engage C-suite stakeholders, and collaborate with product, consulting, and account teams to close and expand strategic contracts.

As Product Manager, you will own the vision and roadmap for Mastercard's Responsible AI and Privacy Enhancing Technologies Toolkit, ensuring product features align with governance, privacy, and regulatory expectations, while driving adoption and product outcomes.

The Enterprise Account Executive, Acquisition role focuses on developing a sales strategy within a territory for new customer acquisition, meeting sales targets, and collaborating with Solutions Architects and Professional Services teams.

The role involves leading data analytics initiatives to improve yield and quality, developing algorithms, enhancing data infrastructure, and collaborating with cross-functional teams to drive innovation in HBM product engineering.

This role focuses on improving productivity in labor operations through program management, data analysis, and implementing AI and automation solutions.

The role involves developing advanced data analytics solutions for yield and quality improvement in HBM product engineering, collaborating with cross-functional teams and driving innovation in AI and machine learning.

The SR Data Scientist will analyze data in semiconductor manufacturing, optimize yields, develop data pipelines, support experiments, and communicate findings using visualization tools.

The role involves designing software and algorithms to analyze large datasets for actionable insights, enhancing production processes and product development.

The Data Scientist will analyze big data to develop predictive models and actionable insights, collaborating with various teams to enhance products and services.

The role focuses on developing product disposition solutions, enhancing yield and reliability through data analysis, and mentoring team members while collaborating across 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.

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.

Lead procurement data strategy to enable agentic AI: design agent-embedded governance, define agent-consumable data requirements, build lineage/catalog foundations, establish machine-readable policies and feedback loops, run experiments, and coach cross-functional stakeholders to operationalize master data and data quality at scale.

The role involves developing AI-driven solutions for semiconductor engineering, focusing on GenAI systems, data pipelines, and machine learning models while collaborating with cross-functional teams.

The Principal Data Scientist will optimize semiconductor yields, develop data pipelines, apply advanced analytics, support experimentation, and communicate findings effectively.

As a Data Scientist, you will optimize semiconductor manufacturing processes through data analysis, develop predictive models, and automate data workflows.

Summer internship applying data science, AI, software engineering, analytics, and automation to manufacturing and business problems. Interns analyze datasets, build dashboards/reports, develop automation or software solutions, and present findings to improve operational efficiency and support digital transformation.

Design, build, test, deploy, and maintain scalable, resilient cloud-based data pipelines and microservices (primarily on AWS). Implement ETL/ELT, data lake/warehouse solutions, support analytics integration, enforce data quality, and follow Agile DevOps practices with CI/CD and IaC.

Act as primary contact for exchanges and market-data vendors, support licensing, ensure compliant data usage, coordinate audits, translate contract terms into operational requirements, and collaborate with technology, legal, and sourcing teams to maintain licensing coverage.

Designs and delivers data architecture solutions, creates secure production code, evaluates technologies, guides architecture governance, and applies AI-assisted practices to improve data frameworks, resiliency, and security.

Own APAC technical product agenda for the firm's Data & AI platform: translate regional business, regulatory, and user needs into requirements and roadmaps; prioritize backlog; define governance, controls, non-functional requirements; partner with engineering and global stakeholders to deliver scalable, production-ready capabilities and measure adoption and outcomes.

Provide high-level administrative support to the Chief Data & Analytics Office: manage complex calendars, arrange domestic and international travel, coordinate VIP logistics and meetings, process expenses and invoices, ensure compliance with policies, handle confidential information, and support special projects and backup coverage for other assistants.

Work with platform users, analysts and data engineers to design, test and document data models, pipelines and BI solutions. Implement data validation, reconciliations and controls, support ad-hoc analysis and external financial reporting to maintain a scalable, reliable data platform.

Work at the intersection of data, operations, and business to identify, prioritise, and resolve data quality and operational issues. Build SQL/Python-based analytics, dashboards, and AI-enabled solutions; perform root-cause analysis; partner with stakeholders to deliver scalable fixes and maintain data governance and controls.

Lead AI/ML adoption across APAC by identifying high-impact use cases, translating business needs into technical delivery plans, driving operationalization of AI capabilities, promoting AI literacy, and enabling cross-functional collaboration to deliver measurable business value.