Top Data Jobs in Singapore

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.

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.

Leads the design and delivery of scalable, secure technology solutions across applications, integrations, and data platforms. Translates business requirements into technical designs, provides hands-on engineering guidance, conducts code and design reviews, and applies architectural and development standards. Partners with business, product, architecture, and infrastructure teams throughout Agile delivery. Oversees data modeling, pipelines, APIs, microservices, cloud and hybrid environments, security, performance, reliability, technical risk resolution, and mentoring of software and data engineers.

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.

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.

Provision, stage, troubleshoot, repair, and maintain bare-metal servers, network devices, and data center infrastructure supporting a global cloud security network. Collaborate with network, platform, installation, and maintenance teams to validate deployments and improve workflows. Translate cloud workloads into hardware requirements, diagnose physical connectivity and platform issues, and visit the Singapore integration center as needed. The role requires strong Linux/Unix, server, network hardware, BIOS, and out-of-band management experience, with scripting and automation skills preferred.

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.

Summer analysts will build and deploy machine learning, generative AI, and agent-based solutions; integrate complex datasets; develop predictive models; run experiments; analyze trends; and deliver actionable insights. They will also support data governance, risk management, data quality, privacy, and regulatory controls while collaborating with agile, cross-functional teams. The program includes mentorship, training, and potential full-time opportunities after graduation.

Develop an AI-enabled automation solution for low-yield analysis in semiconductor memory testing. The intern will study test operations, manufacturing systems, test methodologies, data flows, and lot-disposition processes; apply programming, analytics, visualization, and AI tools; validate improvements in efficiency, accuracy, and consistency; and document findings through a final report and presentation.

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.

Analyzes semiconductor photo manufacturing data to identify factors affecting train size, throughput, cycle time, tool utilization, and reticle availability. The intern will build predictive or simulation models, dashboards, and recommendations to optimize manufacturing performance and factory output. Responsibilities include collecting and validating data, analyzing historical trends, evaluating optimization scenarios, and presenting findings.

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.

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.

Leads Microsoft relationships with enterprise customers, develops strategic account plans, identifies and advances sales opportunities, and drives revenue and customer transformation outcomes. Builds executive relationships, orchestrates cross-functional Microsoft and partner teams, manages pipelines and forecasts, develops business cases, and ensures customer satisfaction and long-term account growth.

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.

Leads procurement analytics, data science, business intelligence, and data governance initiatives, initially supporting the AMEA region. Develops reports, insights, predictive models, data quality rules, governance documentation, and scalable digital solutions. Partners with Procurement, Finance, Supply Chain, Technology, and external providers to improve data quality, automate reporting, enable analytics adoption, and translate complex findings into business recommendations.

Lead Data Scientist developing and maintaining payment-data models to detect fraud, scams, and money laundering across Asia-Pacific. Responsibilities include algorithm research, proof-of-concept development, product prototyping, financial crime model governance, client-facing visualizations, white papers, and regional customer support. The role collaborates with global data science and product teams while building locally tailored capabilities and considering privacy, security, regulation, model accuracy, and performance.

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.

Implement and maintain data governance frameworks, policies, standards, and data quality controls. Support data ownership, stewardship, metadata management, lineage, regulatory compliance, governance forums, stakeholder engagement, training, audits, and enterprise data transformation initiatives for banking clients.

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.

Manage and expand strategic partnerships across Southeast Asia, Japan, Greater China, and South Korea. Recruit and develop GSIs, SIs, agencies, MSPs, OSPs, and other services partners; create joint business plans; drive co-selling, partner-led demand generation, pipeline, revenue, and market expansion. Build executive relationships, lead business reviews, shape services-led go-to-market initiatives, and collaborate cross-functionally on partner activation and customer adoption. Travel across Asia may be required.

Implement and maintain data governance frameworks, policies, standards, and data quality controls. Manage data ownership, stewardship, metadata, lineage, and issue remediation processes. Partner with business, technology, risk, and compliance stakeholders, facilitate governance forums and training, and support audits, regulatory initiatives, and enterprise data transformation programs for banking clients.

Leads Igloo’s enterprise technology solutions portfolio, positioning, productisation roadmap, complex enterprise sales cycles, solution design, and commercial pipeline. Translates platform capabilities into business value for insurers, banks, fintechs, and digital platforms; oversees architecture, integrations, security posture, ROI cases, and implementation handoffs. Builds ecosystem partnerships and develops a solutions and enterprise go-to-market team across Southeast Asia.

Dragonfly is seeking Software Engineers for various portfolio companies, focusing on crypto technologies. Engineers of all experience levels are needed across multiple stacks.

Lead business analysis for an AI and data transformation initiative in banking. Define and improve data enablement processes, translate business and technical requirements, support reusable data products, and address data discovery, governance, quality, lineage, and risk. Coordinate business, data, AI/ML, technology, and risk stakeholders while managing scope, priorities, dependencies, timelines, and delivery blockers. Senior hires lead complex workstreams and contribute to target-state roadmaps.