Top Data Jobs in Singapore

Support EDB Finance by improving and automating incentive administration workflows. Co-design systems and dashboards, review company claims for compliance, perform on-site assessments, prepare reports to senior management, identify policy gaps, and promote incentive governance and risk management.

Support deployment of marketing analytics for Consumer Banking by performing data discovery, building and maintaining predictive models, campaign reporting, segmentation, and campaign execution support. Collaborate with product managers, data engineers, and data scientists to improve marketing effectiveness, automate campaign processes, and translate data into actionable insights for customer acquisition, cross-sell and activation.

The Control and Metrics External Data Analyst will manage external data usage, oversee external data initiatives, and support the Chief Data Officer in implementing the Bank's data strategy.

As a Data Scientist, you'll develop predictive models, analyze large datasets, and collaborate with stakeholders to drive data-informed business decisions.

Lead Enterprise AI initiatives by designing, developing, and deploying AI/GenAI solutions. Partner with data engineers, technologists and business stakeholders to prototype, integrate, validate and benchmark LLM and advanced-model applications, manage expectations, and translate analytic results into business outcomes.

This role involves designing and developing AIML/GenAI solutions, collaborating with engineering teams, and translating business needs into AI-driven solutions.

The intern will design safe processes, analyze behavior for anomalies, track fraudulent accounts, and maintain machine learning models to enhance account safety.

Lead pricing-focused data science for on-demand services: translate business problems into ML solutions, own full ML lifecycle, improve dynamic pricing using econometrics and causal inference, design experiments, monitor models, and communicate results to stakeholders.

The Senior Data Scientist will deliver analytics solutions, develop scalable data products, and work on machine learning and statistical models to solve operational challenges.

Sustain and operate Marvell's Silicon.da test data analytics platform: monitor and resolve data loading and quality issues, support users via ServiceNow/Slack, coordinate with vendors and foundries, maintain documentation, and drive issue closure and operational readiness.

Lead end-to-end delivery of data, analytics, and AI projects on GCP. Manage scope, timelines, risks, cross-functional teams, Agile delivery, stakeholder communication, and governance to ensure solutions meet business objectives.

Lead design and implement enterprise data integrations and movement across systems. Define data contracts, map/transform data, support GenAI data preparation, coordinate deliveries, troubleshoot integrations, and document interfaces while working across platform, application, infrastructure, security teams.

The Technical Services Systems Integration Specialist provides technical support, engages with clients, escalates issues, and mentors junior engineers while focusing on technical implementations across multiple technologies.

Sell high-bandwidth IP transit and value-added services to ISPs and internet companies, generate new revenue, prepare technical/commercial proposals, manage accounts, coordinate cross-functional teams, track sales plans, and support global customers with up to 50% travel.

Own data quality and root-cause analysis for prediction issues: detect anomalies, trace sources, fix processes, automate QA/reporting with Python and SQL, maintain dashboards, and partner with Data Science, Engineering, Product, and Commercial teams to improve prediction performance and customer-facing analytics.

Support data integrations and quality by checking ingested/system-generated data for anomalies, building and running QA checks and automations with Python and SQL, assisting on customer data queries and dashboards, and documenting findings to improve prediction reliability.

Own and operate thousands of mission-critical ETL pipelines, manage and resolve data incidents, validate and remediate data, build automation and tooling to improve reliability, and collaborate with portfolio managers, data scientists, and vendors to ensure accurate, timely data delivery for trading research and execution.

Design, implement, and operate patent and related data pipelines: integrate, parse, store, backup, monitor and optimize data quality and processing performance. Collaborate with data architects to improve scalability, stability, and documentation while ensuring data security and regulatory compliance.

Lead the strategic vision for a data-driven Diagnostics supply chain: centralize supply chain data into a single source of truth, govern global KPIs, deploy BI and process-mining tools, bridge technical architecture and business execution, and drive organization-wide data-driven decision-making across global matrix teams.

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.

Lead end-to-end development and productionization of predictive, prescriptive, and agentic AI analytics across commercial, marketing, finance, and franchise domains. Build scalable ML/LLM solutions, implement RAG and orchestration, partner with stakeholders, and drive adoption and business value through analytics products and MLOps practices.

The role involves leading BIO initiatives, collaborating with sales teams, enabling digital capabilities, and engaging stakeholders to enhance agent productivity and business outcomes.

Support construction and maintenance of overseas advertising data reports and indicators. Develop and maintain data flows for the overseas advertising system. Collaborate with cross-functional teams to translate business requirements into data engineering solutions while ensuring data quality, reliability, and performance.

Analyze transaction and account data to detect card fraud, money mule networks, and suspicious cashflows; build models and dashboards for collections and lending products; automate reporting; support CBS submissions, regulatory reporting and cross-functional stakeholders to improve risk mitigation and collection effectiveness.

Design, build and productionize data and ML models and pipelines; collaborate with data engineers on ingestion, backend services and analytics; assess data for hypotheses and test use cases; document models and transfer modelling capability to internal teams.