Top Data Jobs in Singapore

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.

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.

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.

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 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.

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.

Lead architecture and build of a global, high‑performance data and AI platform. Own design, performance tuning, and SDLC for distributed systems (streaming, storage, indexing) powering real‑time analytics, vector search, and AI-driven products. Mentor engineers and drive engineering rigor and shared tooling.

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 analytics, simulation, optimization, and digital twin solutions for semiconductor manufacturing. Develop software, models, and AI/LLM-enabled tools to analyze large unstructured data, optimize operations, and communicate insights to product and operations teams while driving global collaboration and continuous improvement.

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.

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

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.

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.

Design and deploy computer vision and deep learning models (CNN, LSTM, transformers) for industrial inspection and anomaly detection; extract, clean, and analyze large multimodal datasets; implement MLOps (MLflow, Airflow, Docker) for production deployments; collaborate across engineering, operations, and quality teams; mentor engineers and contribute research outputs and technical documentation.

As a Computer Vision Engineer, you will develop image processing solutions, apply machine learning techniques, and collaborate on engineering projects to enhance manufacturing innovation.

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.

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

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.