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.

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.

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 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.

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.

As a Data Scientist at Micron, you'll develop AI-driven solutions in semiconductor manufacturing, build predictive models, and communicate analytical insights to stakeholders.

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.

Hands-on learning internship exposing the candidate to semiconductor wet-process manufacturing, process flows, equipment behavior, and basic manufacturing data analysis. Support data review, evaluate process stability, document observations, participate in technical discussions, and deliver a final report and presentation summarizing analysis, findings, and learned concepts.

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.

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 machine learning, simulation, and optimization models to analyze manufacturing, equipment, and process data. Build scalable analytics products, dashboards, and automated workflows. Partner with cross-functional engineering teams to improve equipment efficiency, factory productivity, and drive operational excellence through data-driven insights and recommendations.

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.

The Senior Data Scientist will improve semiconductor quality through predictive models and AI solutions, mentor juniors, and communicate insights effectively.

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.