Top Data Jobs in Singapore

Translate stakeholder business challenges into data science projects; clean and preprocess structured and unstructured data; perform statistical analysis, feature engineering, and machine learning; deploy models; create dashboards and visualizations; communicate insights and recommendations; and manage stakeholder feedback and project timelines.

Leads a data annotation team supporting autonomous vehicle perception and machine learning models. Oversees daily operations, productivity, quality, SLAs, workflow efficiency, hiring, onboarding, performance management, coaching, and employee development. Uses data-driven problem solving and automation to remove bottlenecks. May manage external vendors, annotation metrics, and projects across time zones.

Evaluate AI models and LLM systems for performance, safety, fairness, explainability, and policy compliance. Conduct risk assessments, test safeguards such as prompt-injection defenses, maintain governance records, develop Python-based evaluation and monitoring tools, and translate Responsible AI requirements into practical controls. The role also coordinates remediation, prepares risk recommendations, supports technical assurance, and transfers evaluation procedures and documentation to government agency staff.

Leads enterprise analytics solutions across business, product, finance, marketing, and technology. Responsibilities include developing data models, metric definitions, pipelines, dashboards, experimentation frameworks, and executive data narratives; diagnosing data and system issues; standardizing metrics; applying advanced analytics and AI tools; mentoring analysts; and establishing analytics best practices.

Monitors security alerts, investigates incidents, assesses vulnerabilities, implements security controls, supports penetration testing, and maintains compliance with standards such as GDPR, ISO 27001, NIST, and CIS. The role manages security tools, documents incidents, supports remediation, conducts risk assessments, develops security practices, and contributes to employee security awareness. It collaborates with IT and cross-functional teams to protect systems and digital assets.

Supports ETL and data pipeline development, monitoring, validation, quality checks, and integration across multiple systems. Automates recurring data processing and reporting using SQL and Python, maintains technical documentation, assists with ad hoc data requests, troubleshoots data issues, and collaborates with engineers, analysts, and business stakeholders.

Administer SKAN AI and ProHance platforms, develop Power BI dashboards and automated reporting, analyze operational and workforce data, and identify process improvement opportunities. The role supports process intelligence, productivity analysis, workflow automation, system integrations, business application development, and data-driven recommendations. Responsibilities also include stakeholder engagement, user training, documentation, governance, platform maintenance, and translating business requirements into scalable analytics and process optimization solutions.

Develops and deploys advanced analytics and machine learning solutions across operational datasets. Responsibilities include data extraction, cleaning, feature engineering, exploratory analysis, model development, dashboard creation, production monitoring, retraining, documentation, and system integration. The role requires translating analytical findings into actionable insights for engineering and operations partners while working across the full data science lifecycle.

Leads technical AI risk management for the bank’s AI ecosystem, including GenAI, foundation models, RAG architectures, AI platforms, and agentic systems. Defines technical control expectations, risk indicators, monitoring, access, security, resilience, and data protection requirements. Provides independent challenge to technology and engineering teams, evaluates emerging AI risks and regulations, and supports the evolution of AI governance frameworks and regulatory engagement.

Analyzes complex supply chain data to diagnose performance drivers, evaluate planning scenarios, quantify trade-offs, and deliver evidence-based recommendations. The role uses Python, SQL, statistical methods, scenario modeling, and sensitivity analysis while collaborating with planners, data scientists, and automation teams. It translates ambiguous business problems into structured analytical frameworks and validates model outputs against operational constraints. The position focuses on decision support and analytical problem-solving rather than production machine-learning engineering, data pipelines, or application development.

Analyze inkjet product event and printer telemetry data to generate customer usage insights, enable predictive analytics, and create Power BI dashboards monitoring trends and customer experience behavior. The internship requires full-time availability for 5–6 months and strong data analysis, creative thinking, and proficiency with office productivity tools.

Supports supply chain data collection, cleaning, organization, analysis, reporting, statistical analysis, and visualization. The intern identifies trends, collaborates with team members, contributes to data-driven process improvements, and develops practical analytics experience. The role requires a 5–6 month full-time commitment beginning in May 2027.

Drive enterprise sales of FactSet’s data and off-platform technology solutions to buy-side, sell-side, and strategic partner clients. Identify opportunities, manage sales pipelines, develop value propositions, collaborate with sales, implementation, consulting, and product teams, and serve as a subject-matter expert on financial data workflows. Build client relationships, deliver training, provide product feedback, and develop strategies that support client satisfaction, retention, and revenue growth.

Designs and delivers enterprise Data, Generative AI, and Agentic AI solutions for customers. Responsibilities include architecting modern data platforms, AI agents, RAG and orchestration workflows; implementing secure, scalable production systems; applying DevOps, MLOps, observability, and Responsible AI practices; leading technical workshops and customer co-development; mentoring engineers; and creating reusable architectures, accelerators, and engineering patterns.

Lead corporate communications across global markets, including corporate narrative, media relations, crisis and issues management, executive communications, reputation management, and multi-market coordination. The role develops messaging, oversees media engagement and agencies, advises executives and country teams, and ensures consistent communications standards. At Director level, it also includes team leadership, operating-model design, and strategic counsel to senior leadership.

Leads the global marketing function’s strategy, integrated planning, brand management, campaigns, partnerships, digital and content initiatives. Makes investment and prioritization decisions across markets, manages agencies, coaches marketing teams, and partners with Communications and PR. The role requires translating company strategy and go-to-market priorities into international marketing plans while managing budgets, senior stakeholders, and cross-functional execution in a fast-growing, matrixed organization.

Designs and maintains scalable automated testing frameworks for web applications, APIs, databases, and end-to-end processes. Develops test strategies, plans, cases, and quality standards; integrates automated tests into CI/CD pipelines; manages defects and quality metrics; and supports Agile delivery. The role collaborates with technical and business stakeholders, drives continuous improvement, and mentors junior QA professionals.

Manage health, safety, and environmental compliance for data center construction projects. Responsibilities include conducting site visits, safety walks, audits, inductions, incident investigations, safety observations, corrective-action follow-up, and HSE reporting. The role supports project management across multiple sites, coordinates with contractors and stakeholders, and promotes zero-incident performance.

Build and maintain scalable data engineering infrastructure for scientific, sensor, and operational data. Responsibilities include feature engineering pipelines, data quality monitoring, versioning, lineage, drift detection, data contracts, governance, sensor ingestion, synthetic data pipelines, and MLOps data layers. The role requires strong Python, SQL, pipeline design, and data lifecycle fundamentals, with integration into AWS-based AI platforms and progressive ownership of real-time systems.

Build the intelligence layer of a railway data platform by developing end-to-end machine learning solutions for time-series forecasting, anomaly detection, and operational decision intelligence. Responsibilities include data discovery, feature engineering, model deployment, monitoring, scalable data pipelines, automated maintenance alerts, and AI-agent reasoning. The role also involves translating model outputs into dashboard requirements, communicating uncertainty to stakeholders, and collaborating with designers, product managers, and railway engineers.

Leads end-to-end data centre construction projects, including planning, budgeting, scheduling, design coordination, construction oversight, contractor management, commissioning, handover, safety, and quality assurance. The role oversees critical infrastructure such as electrical distribution, backup power, cooling, fire protection, and BMS/DCIM systems while managing risks, changes, governance, and stakeholder communication.

Lead data centre project delivery from planning through completion, managing schedules, budgets, quality, safety, statutory compliance, procurement, consultants, contractors, stakeholders, reporting, and site coordination. Develop junior team members, oversee multidisciplinary teams, maintain master and short-term programmes, manage inspections, resolve issues, and ensure project milestones and objectives are achieved.

Build and maintain scalable data platforms, databases, ETL processes, and data pipelines across relational and NoSQL systems. Integrate data from multiple sources, optimize SQL queries and database performance, perform data quality checks, troubleshoot complex data issues, and support secure, compliant healthcare data processing. The role also includes testing, code reviews, architecture optimization, and collaboration with technical and non-technical stakeholders.

Develop and maintain performant kdb+ applications for capital-markets clients, working with real-time and historical time-series data. Collaborate with business users, project managers, and senior developers to define requirements and deliver solutions. Analyze large-scale data, produce actionable reports, support testing and maintenance, and mentor junior developers. The role requires strong q and kdb+ expertise, Unix/Linux experience, software development lifecycle knowledge, and effective communication.

Lead technical delivery of client projects by designing, implementing, scaling, and maintaining high-performance kdb+ architectures for billions of time-series data rows. Collaborate with quants, traders, and analysts, mentor developers, own the full development lifecycle, and drive innovation in kdb+, q, and related technologies. The role requires strong Unix/Linux skills, capital markets experience, technical leadership, and expertise in production kdb+ systems.