Top Data Jobs in Singapore

Design, develop, automate, and maintain Power BI supply chain dashboards by extracting and transforming data from MRP/enterprise sources. Perform ETL, data quality checks, reporting, stakeholder engagement, project coordination, and provide insights to improve supply chain visibility and operations.

Build, automate, and operate enterprise-scale data platforms using Databricks, Snowflake, Microsoft Fabric, Kubernetes, and cloud technologies. Responsibilities include Infrastructure as Code, CI/CD, GitOps, platform provisioning, reliability engineering, observability, security, governance, incident remediation, performance optimization, and FinOps. The role supports multi-environment deployments and self-service capabilities for data engineering and analytics teams.

Supports Singapore arts and culture data projects by extracting, cleaning, transforming, analyzing, and modeling data. Builds dashboards and visualizations using Excel, AWS QuickSight, SQL, Python, and Tableau; documents data practices; and contributes to a new data warehouse and visualization platform. The role also provides exposure to stakeholder engagement, knowledge management, and organizational adoption of data solutions.

Supports data centre development and acquisition advisory work through site assessment, technical feasibility studies, capacity planning, utility reviews, market and technical research, data analysis, and preparation of reports and presentations. Collaborates with engineering and multidisciplinary teams on greenfield developments and existing asset evaluations while developing insights for strategic and client-facing decisions.

Supports data centre client onboarding, account transitions, operational readiness, SOP documentation, asset validation, CMMS updates, stakeholder coordination, reporting, and administrative project activities across Singapore and the APAC region.

Leads APAC data center development opportunities from site identification through land acquisition and execution readiness. Oversees technical and commercial due diligence, feasibility, utility strategy, permitting, risk management, governance, investment approvals, and portfolio reporting. Coordinates internal teams, consultants, landowners, utilities, regulators, and other stakeholders while preparing business cases and investment recommendations for executive leadership. Provides oversight of budgets, schedules, contracts, risks, and development pipeline performance.

Develop and deploy agentic AI solutions using Python and Azure ML, translate business needs into scalable data science systems, and help architect an open-source framework for autonomous agents. The role includes rapid experimentation, production deployment, customer collaboration, mentoring researchers and developers, contributing to open-source and academic communities, and publishing technical work.

Leads a logistics data and analytics team, setting the analytics roadmap and delivering insights on geographic operations, fleet performance, delivery efficiency, and cost optimization. Develops performance metrics, semantic data models, AI-enabled data products, forecasting solutions, and decision-science analyses. Partners with senior logistics and operations stakeholders, communicates executive-ready recommendations, and builds a high-performing analytics organization.

Leads concurrent client projects and programs from concept through implementation, managing scope, budgets, schedules, risks, quality, change control, stakeholders, escalations, and client satisfaction. Coordinates project teams, supports presales proposals, develops client relationships, and ensures successful transition to operations while achieving business objectives and strategic benefits.

Analyze large datasets using SQL and Python, maintain recurring analyses and data products, perform quality checks, investigate discrepancies, and identify trends. Collaborate with Research, Engineering, and Product teams to resolve data issues, document findings, and improve analytical workflows through automation and AI tools. The role is designed for an early-career analyst who is detail-oriented, curious, and interested in using data to inform investment decisions.

Connects marine, data, and delivery teams by integrating and validating shipping data, analyzing large datasets with SQL, translating business needs into technical requirements, and coordinating Agile delivery. Applies marine and dry bulk shipping expertise across vessels, voyages, ports, cargoes, bunkers, and operations while supporting predictive analytics, data quality, stakeholder management, testing, and implementation.

Manage and govern the bank’s enterprise Power BI platform, including Microsoft Fabric capacity, workspaces, gateways, access controls, reporting standards, deployment, monitoring, and performance optimization. Lead Power BI development practices, SQL-based data integration, Python automation, Jenkins workflows, and release management. Partner with engineering, infrastructure, security, governance, vendors, and business stakeholders to deliver secure, scalable, compliant analytics solutions while mentoring developers and coordinating concurrent initiatives.

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.

Analyze manufacturing, inventory, and supply chain data; monitor supply risks; support forecasting; review planning parameters and system compliance; present insights; build automated reports and dashboards; use AI tools to improve processes; and contribute to continuous improvement and digital transformation initiatives.

Owns the lifecycle and prioritization of corporate data products, including dashboards, datasets, analytics, and AI-enabled solutions. Partners with business, engineering, data science, and AI teams to translate business needs into use cases, coordinate delivery, drive adoption, and measure value. Leads data stewardship and quality efforts by establishing definitions, rules, ownership, and governance practices across Corporate Functions.

Supports APAC marketing analytics for Disney+ by measuring campaign performance, designing tests, analyzing subscriber cohorts, building dashboards, maintaining data pipelines, and translating findings into recommendations. Uses SQL and Tableau to analyze marketing channels including paid media, email, push, and in-app campaigns. Collaborates with regional marketing stakeholders and local teams to improve acquisition, engagement, retention, and winback initiatives.

Supports global sustainability data collection, quality assurance, reporting, dashboard maintenance, and property onboarding across UL 360 and Greenview Portal. Coordinates with international property teams and the parent company, resolves data discrepancies, prepares sustainability reports, researches best practices, and assists with sustainability initiatives. The role requires strong analytical, communication, organizational, and data accuracy skills, plus an interest in environmental impact and sustainability frameworks.

Intern will design and optimize Generative AI use cases for risk exposure, anomaly, fraud, and red-flag detection. Responsibilities include prompt engineering, testing, validation, automation, data analysis, dashboard development, visualization, and data mart maintenance. The role supports audit effectiveness, operational efficiency, reporting, and data-driven decision-making using Python, SQL, and visualization tools.

Leads Manulife Singapore’s enterprise AI, data, analytics, governance, and transformation strategy. Oversees data platforms, AI solutions, advanced analytics teams, process redesign, regulatory alignment, portfolio delivery, and value realization. Builds AI literacy, strategic partnerships, high-performing teams, and responsible innovation capabilities while engaging executives, regulators, and industry bodies.

Develop AI, machine learning, data analytics, automation, and agentic AI solutions for global financial markets, supporting trading and sales teams. Responsibilities include predictive model development, exploratory analysis, data pipelines, dashboard creation, data integrity management, and analytics for financial products and clients. The role also evaluates new data science approaches, partners with business and operations teams, and leads junior team members.

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.

Develop and deploy AI solutions including generative AI copilots, recommendations, RAG, and agentic AI systems. Build predictive and generative machine learning models using structured and unstructured data, apply advanced visualization and storytelling, and collaborate with business and technical teams on cloud, MLOps, and real-time data initiatives.

Design, build, and ship agentic AI applications that answer business questions over governed data. Implement RAG, NL-to-query, tool/function calling, and evaluation/guardrails. Partner with data teams on metrics, models, pipelines, and apply strong data governance and security for financial services.

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.