Top Data Jobs in Singapore

Support investment teams by acquiring, cleaning, and analyzing diverse datasets to test investment theses. Learn fundamental investor approaches, identify key data drivers and inflection points, and present research findings. Work with existing and new data to generate insights and decision-support tools for sector-focused investment research.

The Senior Data Scientist will develop predictive models, implement machine learning solutions, optimize production processes, and mentor junior staff to enhance data-driven decision-making in semiconductor manufacturing.

Lead advanced data analytics and ML initiatives to improve yield, quality, and reliability for HBM products. Develop ML/DL models, build data infrastructure, integrate solutions into disposition workflows, mentor team members, and collaborate cross-functionally to optimize KPIs including quality, cost, cycle time, and scale.

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.

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.

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.

Design and implement advanced smart manufacturing solutions in scheduling, mathematical optimization, and discrete event simulation. Build data analytics and applications to provide actionable insights, collaborate globally to deploy solutions across manufacturing sites, and integrate AI/OR hybrid methods to improve production performance and benchmarking.

The role involves leading BIO initiatives, collaborating with sales teams, enabling digital capabilities, and engaging stakeholders to enhance agent productivity and business outcomes.

Lead data engineering and process-mining initiatives to capture Digital Footprints, generate actionable visualizations and AI-driven optimizations, standardize scalable digital processes across Front-End sites, and track productivity and labor-hour savings via dashboards and cross-functional execution.

Develop end-to-end data science and ML solutions for semiconductor manufacturing to improve yield, quality, cycle time, and reduce cost. Work across the full pipeline from data acquisition and cleansing through model building, deployment, visualization, and collaboration with international engineering teams. Leverage statistical modeling, deep learning, generative AI techniques, and manufacturing domain knowledge to drive high-impact projects.

Design and deploy computer vision and deep learning solutions for manufacturing inspection and monitoring. Extract and clean large datasets, build multimodal AI models, apply MLOps for production deployment, and collaborate with cross-functional teams to translate research into operational systems and documentation.

Apply data science and ML to semiconductor yield and process optimization: build data pipelines, analyze sensor and tool logs, support DOE, develop models, and create dashboards to guide engineering improvements.

Lead global initiatives to improve facilities cost data visibility, metrics alignment, reporting, and process enablement. Drive data governance, reporting development, and sustainable reporting operations across Global Facilities to improve decision-making and business insight.

As a Data Scientist, you will analyze large datasets, develop predictive models, and improve existing tools while collaborating with teams for data analysis projects.

The IT Engineer (Data Services) is responsible for managing and optimizing database systems, ensuring data availability, performance, and compliance, while also developing automated solutions and collaborating with site customers.

Join Micron as a Data Scientist to apply data analysis and machine learning expertise toward innovations in memory, storage, AI, and related products. The posting emphasizes company mission, inclusion, and application process; specific role responsibilities and qualifications are not listed.

The Engineering Director will lead software engineering teams, set technical direction, ensure product quality, and foster innovation and collaboration.

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.

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.

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.

The role involves developing technology policies, conducting research, coordinating AI efforts, and engaging with stakeholders to strengthen Singapore's digital economy.