Top Data Jobs in Singapore

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.

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.

Own regional data center vendor strategy and end-to-end colocation deals, including planning, RFPs, commercial negotiations, execution, vendor relationship management, QBRs, budgeting, spend review, and invoice approval. The role coordinates with legal, operations, capacity planning, network strategy, and security teams across multiple geographies to expand and optimize Cloudflare’s data center footprint.

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.

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.

Designs, builds, and operates secure AWS-native data pipelines and platforms for batch and event-driven workloads. Develops Airflow workflows using Python and SQL, implements tested data transformations, and integrates enterprise, security, and operational data sources. The role embeds data quality, lineage, observability, governance, encryption, access controls, and monitoring. It also contributes to CI/CD, infrastructure as code, documentation, code reviews, incident analysis, and cross-functional solutions supporting risk and regulatory needs.

Maintains and enhances investor profiles within Preqin’s private markets database through validation, research, and cross-checking. Builds relationships with finance professionals, supports clients with data and market insights, assists with onboarding, resolves data quality issues, and provides subject matter expertise. The role also supports data operations, publications, research projects, and continuous improvements across global teams.

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.

Lead technology strategy for Microsoft education customers in Singapore. Advise senior executives, develop multi-year transformation roadmaps across AI, cloud, data, security, modern work, and business applications, and coordinate specialists, engineers, customer success teams, and partners. Drive responsible AI adoption, governance, technical wins, workload consumption, partner delivery, customer outcomes, and account growth while providing education-sector thought leadership.

Software Engineers are matched confidentially with Dragonfly portfolio companies hiring across crypto and blockchain. Roles may span backend, frontend, full-stack, mobile, infrastructure, data, security, machine learning, and DevOps. Candidates should have software engineering experience and an interest in crypto, blockchain, or open-source technologies. Startup or crypto-native project experience is preferred. Opportunities may be remote, hybrid, or in-person worldwide.

The role involves developing AI-driven solutions for semiconductor engineering, focusing on GenAI systems, data pipelines, and machine learning models while collaborating with cross-functional teams.

Lead global analytics and data science teams supporting semiconductor manufacturing. Drive yield and process optimization through SQL, sensor, and fabrication-tool data; apply statistical modeling and machine learning to reduce defects; support DOE experiments; build automated dashboards; and manage cross-functional delivery portfolios. Mentor technical talent, improve data quality and platform performance, standardize modern data stacks, and partner with engineering and business leaders on prioritization, risks, and dependencies.

Lead technical strategy with channel partners across ASEAN to build repeatable Microsoft Cloud and AI Workforce offerings (Copilot, Power Platform, Agents). Drive architecture design, workshops, POCs, offer development, partner enablement, and co-sell execution to convert technical capability into scalable revenue and customer outcomes.

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.

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.

Lead data science strategy for AML and financial crime risk controls. Develop AI-powered screening, entity resolution, graph and network intelligence, anomaly detection, transaction monitoring, and proactive discovery of emerging threats. Partner with Product, Engineering, Compliance, and Operations to deliver scalable production controls, influence the roadmap, establish reusable frameworks, and mentor data scientists.

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.

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.

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 global analytics engineering initiatives for electronic trading and research. Design, build, and optimize real-time data pipelines, scalable APIs, analytics libraries, and production trading tools. Apply AI to data engineering, analytics, and SDLC automation while ensuring reliability, testing, security, and continuous delivery. Collaborate with trading and research teams, onboard datasets, mentor engineers, manage workstreams, and establish technical standards across distributed teams.

Lead design and deployment of agentic AI for manufacturing planning and operations. Build multi-agent systems combining LLM reasoning and optimization (MIP, heuristics, simulation), integrate agents with data pipelines and enterprise systems, and deliver solutions for capacity planning, production flow optimization, and decision automation. Partner with planning, operations, data engineering, and MLOps teams to translate domain requirements into deployable AI workflows and reusable orchestration frameworks.

Leads smart manufacturing data science initiatives for the F10 organization, serving as the bridge between site leadership, users, and enterprise teams. Responsibilities include prioritizing requirements, driving solution adoption, balancing local support with network best-practice deployment, evaluating capabilities, coordinating cross-functional projects, monitoring deliverables, and managing competing resources and priorities.

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 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.

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.