Top Data Jobs in Singapore

Operate and engineer database systems supporting manufacturing and engineering. Responsibilities include installation, upgrades, patching, access management, backups, performance tuning, monitoring, incident resolution, disaster recovery, capacity planning, reporting, automation, and business continuity. The role provides on-call support, collaborates with customers on database requirements, improves operational reliability, and evaluates emerging database technologies and AI-assisted tools.

Leads a team developing and productionizing predictive, machine learning, deep learning, and GenAI solutions for semiconductor manufacturing and HBM product engineering. Responsibilities include yield and reliability modeling, large-scale data pipelines, MLOps, model monitoring and drift control, distributed analytics, deployment, and engineering decision support. The role also requires mentoring engineers, setting technical direction, collaborating cross-functionally, and translating model outputs into improvements in quality, cost, cycle time, and manufacturing efficiency.

Analyze semiconductor manufacturing data to identify yield detractors, reduce process variation, and improve device yield. Build data pipelines and automated processes using data from SQL databases, sensors, and fabrication tools. Apply statistical modeling, machine learning, and DOE analysis to solve production issues and support root cause investigations. Create dashboards and reports with visualization tools, collaborate with process engineering teams, and responsibly use AI-enabled tools to improve manufacturing outcomes.

Analyze semiconductor manufacturing, inline, parametric, probe, sensor, and fabrication-tool data to identify yield detractors and reduce defects. Build data pipelines, automate analysis, apply statistical modeling and machine learning, support Design of Experiments, and create dashboards and reports using Dash, Plotly, or Angular. Collaborate with manufacturing, process, equipment, and integration engineers to optimize processes and communicate findings.

Build and productionize machine learning models for semiconductor quality and manufacturing processes. Develop agentic AI, LLM, retrieval-augmented generation, tool-use, and memory capabilities for predictive quality workflows. Create maintainable analytical pipelines, optimize performance and token efficiency, and support testing, deployment, and monitoring. Communicate analytical findings to technical and non-technical stakeholders while applying AI tools responsibly.

Develop and deploy machine learning, statistical modeling, and predictive analytics solutions for semiconductor manufacturing and business operations. Analyze large datasets, build scalable data workflows, conduct exploratory and root-cause analysis, and partner with engineering and software teams to operationalize models. Lead analytics projects, communicate insights to technical and business stakeholders, mentor junior data scientists, and ensure responsible, secure, compliant use of AI and data.

Analyze semiconductor manufacturing data to identify yield detractors, optimize processes, and reduce defects. Build data pipelines from SQL databases, sensor networks, and fabrication logs; apply statistical modeling, machine learning, SPC, DOE, and related analytics. Create automated dashboards and reports using Dash, Plotly, or Angular, and collaborate with manufacturing, equipment, process, and integration engineers to resolve production issues and drive continuous improvement.

Serves as the technical specialist for EHSS data, designing data models, mappings, transformation logic, quality controls, metadata, and reusable datasets. Partners with subject matter experts, IT, and analytics teams to deliver trusted data products supporting reporting, analytics, automation, AI, and operational decisions. Responsibilities include validation, reconciliation, root-cause analysis, testing, deployment readiness, governance, documentation, and long-term sustainment of enterprise data assets.

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.

Develop end-to-end data science and digital twin solutions for manufacturing, planning, and supply chain operations. Responsibilities include business problem framing, data analysis, mathematical optimization, discrete event simulation, software and pipeline development, API and UI creation, deployment, user adoption, benchmarking, and continuous improvement across global manufacturing sites.

Designs and deploys end-to-end data science solutions using large, diverse datasets. Responsibilities include building data pipelines, developing machine learning models, analyzing semiconductor yield and process data, extracting insights with SQL, deploying production AI solutions on GCP, and communicating findings to technical and business stakeholders. The role also involves model monitoring, maintenance, experimentation, and collaboration with product and service teams.

Manages index and market data services, including data licensing, vendor relationships, commercial discussions, and contractual support. Partners with Legal and investment teams to interpret licensing requirements, mitigate risk, monitor regulatory developments, and improve operational efficiency. Develops expenditure reporting to inform business decisions and maintains relationships with internal stakeholders and external data providers.

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.

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.

Leads global data science and analytics teams supporting semiconductor manufacturing. The role applies statistical modeling, machine learning, data engineering, and visualization to optimize NAND process capability, device yield, and operational performance. Responsibilities include building data pipelines, analyzing manufacturing and equipment data, designing experiments, developing automated tools, mentoring technical talent, and managing cross-functional analytics programs. Strong semiconductor manufacturing expertise, Python, SQL, cloud platforms, and advanced engineering analytics experience are required.

Maintain Cloudflare’s global data center network and operational availability across remote colocation facilities. Responsibilities include configuring network infrastructure, coordinating contractors and vendors, managing deployments and decommissions, creating SOPs and operational documentation, improving processes through automation and AI tools, supporting troubleshooting and root-cause analysis, and participating in a 24/7 on-call rotation. The role also involves basic program management, cross-functional collaboration, and limited travel.

Provision, stage, troubleshoot, repair, and maintain bare-metal servers, network devices, and data center infrastructure. Collaborate with engineering and installation teams to validate builds, improve deployment workflows, and optimize staging processes for a global cloud security network. The role requires expertise in hardware configurations, physical connectivity, Linux/Unix, BIOS, out-of-band management, virtualization, scripting, and build automation. Occasional travel to a Singapore integration center is required.

Develop advanced analytics solutions for High Bandwidth Memory manufacturing. Analyze engineering datasets, build automated data pipelines and monitoring workflows, apply statistical and machine learning methods to detect deviations, investigate process-to-product relationships, and create dashboards that improve yield, quality, reliability, and performance insights. The intern will present findings to engineering stakeholders and gain experience in semiconductor product engineering, manufacturing analytics, and AI-enabled engineering solutions.

Develops computer vision and deep learning solutions for semiconductor manufacturing and environmental monitoring. Responsibilities include building anomaly detection and automated inspection systems, applying generative AI and multimodal models, analyzing large datasets, deploying and monitoring production models, and communicating insights. The role also leads cross-functional collaboration, mentors engineers, and manages end-to-end data science initiatives.

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.

Develop and deploy optimization, simulation, and ML models to improve semiconductor factory capacity, throughput, and cycle time. Partner with cross-functional manufacturing and engineering teams to analyze constraints, run scenario analyses, and deliver data-driven recommendations integrated into production pipelines and decision-support tools.

Develop computer vision and deep learning systems for industrial inspection, anomaly detection, and environmental monitoring. Build and deploy CNN, LSTM, transformer, generative AI, and multimodal models using large datasets from SQL, cloud, and image sources. Apply MLOps practices for monitoring, drift detection, versioning, and testing. Collaborate with engineering, operations, and quality teams while communicating results, managing projects, and mentoring engineers.

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.

Partners with manufacturing, planning, and supply chain stakeholders to solve complex operational problems. Designs and deploys digital twins using operations research, mathematical optimization, and discrete event simulation. Develops production-grade applications, data pipelines, models, APIs, and user interfaces. Converts large datasets into decision-support tools, drives solution deployment across global manufacturing sites, and collaborates with data scientists, AI engineers, product owners, and domain experts.

Operate and maintain critical database systems supporting manufacturing and engineering. Responsibilities include database installation, upgrades, patching, access management, backups, monitoring, performance tuning, incident resolution, disaster recovery, capacity planning, reporting, automation, and business continuity. The role provides on-call support, collaborates with customers on database requirements, improves operational reliability, and evaluates emerging database technologies and AI-assisted tools.