Top Data Jobs in Singapore

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.

Lead credit risk analytics for business lending: monitor portfolio performance, identify emerging risks, support portfolio-level credit strategies, prepare reports and dashboards, collaborate with operations and product, contribute to stress testing, regulatory reporting, and governance.

Drive data science for Coinbase consumer products by analyzing ambiguous business problems, defining metrics, building experimentation and causal frameworks, developing advanced models, maintaining ETL pipelines, and translating insights into product strategy. The role requires SQL and Python expertise, statistical rigor, data visualization, stakeholder influence, code reviews, and responsible use of generative AI tools.

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.

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

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