Top Data Jobs in Singapore

Supports technology-enabled transformation projects for energy and commodities trading clients. Responsibilities include designing operating models and workflows, shaping technology strategies and platform roadmaps, strengthening data and analytics foundations, selecting and implementing CTRM/ETRM systems, applying automation and AI, and developing risk and control frameworks. The role also supports project planning, deliverables, stakeholder relationships, and business development activities.

Lead design and implementation of cloud DevOps strategy for global Data and AI infrastructure. Build observability and detection capabilities, enable self-service tooling for data teams, manage scalable, secure infrastructure, and align DevOps practices and standards across geographies.

Prospect into Singapore public sector organizations through phone, email, social selling, events, and other outbound channels. Research accounts, identify stakeholders, tailor outreach, qualify prospects, and create sales opportunities in partnership with Account Executives and partners. The role focuses on achieving activity, meeting, pipeline, and new-logo targets while engaging technology stakeholders across IT, security, cloud, data, and engineering.

Lead data governance and management consulting engagements for financial services clients. Design governance frameworks, maintain data dictionaries, glossaries, metadata catalogs, lineage, and classification standards, and support data quality, architecture, MDM, and regulatory compliance initiatives. Partner with executives, risk, compliance, technology, and business stakeholders while delivering Agile data transformation programs and governance reporting using enterprise platforms such as Collibra, Informatica, and Microsoft Purview.

Design and implement analytics, simulation, optimization, and digital twin solutions for semiconductor manufacturing. Develop software, models, and AI/LLM-enabled tools to analyze large unstructured data, optimize operations, and communicate insights to product and operations teams while driving global collaboration and continuous improvement.

The Senior Data Scientist will improve semiconductor quality through predictive models and AI solutions, mentor juniors, and communicate insights effectively.

Develop production-ready computer vision and multimodal AI systems for interactive applications. Responsibilities include designing algorithms for detection, tracking, segmentation, classification, action recognition, and video understanding; training and optimizing models; managing annotated datasets; evaluating performance; enabling real-time or edge inference; integrating models into production; and collaborating with engineering and data teams. The role also involves research, documentation, testing, and consideration of ethical and regulatory implications.

Manage complex banking document and data processing, account servicing, credit card operations, regulatory requests, reconciliations, exception handling, audit readiness, vendor performance, quality controls, and workflow improvements. Serve as a subject matter expert, support junior staff, conduct root cause analysis, maintain operational accuracy, and provide backup Team Lead coverage.

Lead architecture, reliability, scalability, modernization, and operational excellence for enterprise data and visualization platforms. Responsibilities include building Big Data infrastructure, managing Trino, Superset, Tableau, and related tools, migrating legacy applications to cloud-native environments, improving CI/CD, implementing observability with ELK, Prometheus, and Grafana, optimizing capacity and costs, overseeing production support, and mentoring SRE and DevOps teams in a regulated banking environment.

Own logistics analytics problems end to end, delivering insights and advanced analytical solutions for fleet sizing, routing, staffing, demand planning, and operational optimization. Apply simulation, optimization, statistical modeling, causal inference, and experimentation methods. Build scalable data products, collaborate with engineering and data teams, mentor junior analysts, and communicate recommendations to senior stakeholders.

Supports enterprise data engineering initiatives, including developing and maintaining data pipelines, preparing and validating data, improving data quality, automating manual processes, analyzing and reporting data, and exploring AI-driven solutions. Collaborates with engineering and business teams to gather requirements, translate them into data solutions, conduct testing, document work, and support continuous improvement across enterprise-scale banking data platforms.

Supports regional Disaster Recovery initiatives through incident and exercise data analysis, dashboard development, reporting, documentation, checklist improvement, knowledge management, and automation. The intern will help improve recovery readiness, operational resilience, process efficiency, and decision-making while collaborating with technology teams across the organization.

Leads cross-functional special projects involving air network planning, scheduling, airline partner performance, and operational analytics. Develops optimization models, analytical frameworks, automation tools, scripts, and lightweight products to improve routing, capacity allocation, scheduling efficiency, and planning workflows. Partners with engineers, operations teams, and management to implement solutions, establish best practices, and scale innovative approaches across the organization.

Build and maintain scalable data pipelines for scientific, sensor, and operational data used in machine learning. Responsibilities include feature engineering, data quality monitoring, versioning, lineage, drift detection, data contracts, governance, sensor ingestion, synthetic data infrastructure, and MLOps data-layer components such as feature stores and dataset registries.

Design, implement, and maintain data-driven processes and analytics solutions. Collect, cleanse, automate, and prepare data from disparate sources. Build KPIs and dashboards, apply statistical and machine learning methods, collaborate with stakeholders, perform testing and deployment, and train users to enable data-driven decision making and process improvements.

As a Computer Vision Engineer, you will develop image processing solutions, apply machine learning techniques, and collaborate on engineering projects to enhance manufacturing innovation.

As a Senior Data Scientist, you'll analyze large datasets using advanced mathematical and statistical methodologies to derive actionable insights and optimize semiconductor operations.

The IT Engineer (Data Services) is responsible for database system management, performance tuning, compliance, incident resolution, and automation of database operations.

Lead development and deployment of deep learning and probabilistic models for multi-physics engineering problems. Architect scalable data pipelines, collaborate with simulation engineers, drive R&D, mentor junior team members, and work directly with customers to integrate models into production, including onsite customer collaboration and presentations.

Leads mechanical engineering for critical infrastructure and data center projects across APAC. Reviews and optimizes mechanical, plumbing, and fire protection designs; coordinates consultants, contractors, vendors, and cross-functional teams; supports thermal, hydraulic, CFD, and acoustic studies; manages technical documentation, quality reviews, scope changes, schedules, costs, and compliance with applicable standards.

Build and operate scalable scientific data pipelines and services integrating laboratory, OMICS, imaging, assay, formulation, consumer, and performance data. Implement metadata, provenance, versioning, identity resolution, access, semantic, and data-quality controls. Develop interfaces connecting repositories, knowledge graphs, analytical tools, computational models, and AI workflows. Monitor reliability, availability, dependencies, and performance while resolving bottlenecks and providing operational support for trusted analytical data foundations.

Build and operate scalable financial market data pipelines for Binance stock products and AI applications. Responsibilities include sourcing, ingesting, modeling, processing, storing, and serving real-time and historical data; designing multi-market data frameworks; optimizing Flink pipelines; and establishing quality, reconciliation, monitoring, lineage, and recovery systems. The role also evaluates vendors and data sources, defines data contracts, and collaborates with trading, product, AI, legal, and compliance teams.

Conduct technical research at the intersection of AI and blockchain, exploring LLMs, agents, on-chain data, trading, and intelligent applications. Build reproducible prototypes and validate innovative ideas through proof-of-concepts and product demonstrations. Track industry developments, analyze research findings, document conclusions, and collaborate with algorithm and engineering teams to advance concepts toward implementation.

Leads architecture, security, operations, cost management, and governance for an enterprise GenAI and data platform on Google Cloud. Designs LLM agents, RAG solutions, secure tool-enabled workflows, private networking, IAM, observability, and capacity models. Oversees distributed data pipelines, cloud migration, performance optimization, risk approvals, and engineering standards while mentoring teams and engaging security, vendors, and business stakeholders.

Own the data platform product roadmap, including multi-chain ingestion, on-chain parsing, token and market data, APIs, datasets, analytics tools, governance, quality monitoring, and metric frameworks. Partner with engineering, backend, algorithm, BI, risk, growth, and business teams to deliver reusable data products. The role requires substantial product management experience, hands-on Web3 knowledge, understanding of blockchain data models, SQL proficiency, and familiarity with data infrastructure and analytics tooling.