Top Data Jobs in Singapore

Design and build scalable AWS data lakehouse platforms, including ETL/ELT pipelines, Apache Iceberg or S3 Tables implementations, data quality frameworks, governance, CI/CD, infrastructure as code, and production deployments. Optimize performance, cost, reliability, and scalability while documenting solutions and communicating architecture to technical and non-technical stakeholders.

Supports data collection, validation, cleansing, transformation, dashboard maintenance, exploratory analysis, reporting, documentation, and process improvement. Collaborates with operations, marketing, and revenue management stakeholders on analytics requests while developing skills in Power BI, Python, SQL, and hospitality analytics.

Lead data discovery, governance, and master data management for supply chain. Define processes, KPIs, and quality metrics; translate business needs into data requirements; resolve data issues; run workshops and collaborate across technical and business teams to improve data quality and implement solutions.

Own data product management across event tracking, data modeling, metric definition, dashboards, data governance, and BI solutions. Partner with business teams to understand needs, design end-to-end data solutions, standardize metrics, improve data quality, and drive adoption of data tools. The role requires strong cross-functional leadership, data ecosystem expertise, analytical problem-solving, and English and Mandarin Chinese proficiency.

Design, build, and maintain data models, ETL pipelines, dashboards, and APIs to support industry models, research, and consulting. Ensure data accuracy, completeness, and observability while integrating new datasets and cloud infrastructure. Collaborate with analysts and stakeholders to deliver robust, modular production data systems with minimal supervision.

Supports APAC subscriber analytics for Disney+, using SQL, Python, Excel, Tableau, and data warehouses to produce reports, clean and analyze data, visualize insights, improve dashboards and pipelines, and inform marketing, acquisition, retention, strategy, finance, and commerce decisions.

Lead high-impact data science initiatives from problem definition through production deployment and iteration. Design and deploy machine learning, AI, and analytical solutions, translate ambiguous business problems into measurable objectives, and partner with senior stakeholders on strategy and roadmaps. The role also provides technical leadership and mentorship across the data science community. Strong Python, SQL, statistics, machine learning, production modeling, and large-scale data experience are required.

Analyze billions of user behavior, fund-flow, and market records using PySpark, Hive, Trino, and SQL. Build feature pipelines, investigate data anomalies, and develop and evaluate machine learning models for risk detection. Translate model performance into business metrics such as detection rate, false-positive rate, and latency while owning a project stream end to end.

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 AML data science and analytics initiatives: perform strategic data analysis, statistical modeling on large datasets, consult with business stakeholders, drive system changes from requirements to implementation, improve data quality, and mentor junior staff while managing risk and compliance.

Analyze large-scale gaming datasets using statistics, machine learning, forecasting, recommender systems, generative AI, experimentation, causal inference, and econometrics. Collaborate with product and engineering teams to build data-driven solutions, deliver stakeholder insights, and support innovative gaming applications. Contribute to technical documentation, knowledge sharing, and cross-team collaboration.

Develop and deploy machine learning models for influencer marketing, including creator discovery, content understanding, campaign prediction, and optimization. Clean and structure large datasets, engineer features, design experiments, integrate models into production APIs and inference workflows, and monitor performance. Collaborate with product, engineering, and business teams to translate ambiguous problems into scalable AI-powered product capabilities.

Supports overseas advertising data strategy by analyzing industry, advertiser, and campaign data to identify growth and efficiency opportunities. Collaborates with data science and recommendation teams on optimization strategies and reusable analytical methods. Contributes to an internal diagnostics platform, developing monitoring and attribution capabilities across the ad-delivery funnel to improve issue diagnosis and decision-making.

Develop audience segments, customer profiles, and predictive models using behavioral, transactional, viewing, and digital data. Apply statistical and machine learning methods for clustering, churn, propensity, content affinity, and customer value analysis. Conduct exploratory analysis, evaluate models, support campaign measurement, and translate findings into business recommendations. Collaborate with Marketing, Product, Media Sales, and Content teams while presenting insights to technical and non-technical stakeholders.

Support sustainability reporting, environmental data collection and validation, greenhouse-gas inventory preparation, performance analysis, dashboards, research, and regional sustainability projects. The intern will coordinate with stakeholders, improve reporting and data-management processes, and assist with sustainability workshops and initiatives. The role provides practical exposure to ESG disclosures, carbon accounting, data visualization, decarbonization, sustainable real estate, and project coordination.

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.