Top Data Jobs in Singapore

Lead data engineering and process-mining initiatives to capture Digital Footprints, generate actionable visualizations and AI-driven optimizations, standardize scalable digital processes across Front-End sites, and track productivity and labor-hour savings via dashboards and cross-functional execution.

Develop end-to-end data science and ML solutions for semiconductor manufacturing to improve yield, quality, cycle time, and reduce cost. Work across the full pipeline from data acquisition and cleansing through model building, deployment, visualization, and collaboration with international engineering teams. Leverage statistical modeling, deep learning, generative AI techniques, and manufacturing domain knowledge to drive high-impact projects.

Design and deploy computer vision and deep learning solutions for manufacturing inspection and monitoring. Extract and clean large datasets, build multimodal AI models, apply MLOps for production deployment, and collaborate with cross-functional teams to translate research into operational systems and documentation.

Apply data science and ML to semiconductor yield and process optimization: build data pipelines, analyze sensor and tool logs, support DOE, develop models, and create dashboards to guide engineering improvements.

Lead global initiatives to improve facilities cost data visibility, metrics alignment, reporting, and process enablement. Drive data governance, reporting development, and sustainable reporting operations across Global Facilities to improve decision-making and business insight.

As a Data Scientist, you will analyze large datasets, develop predictive models, and improve existing tools while collaborating with teams for data analysis projects.

The IT Engineer (Data Services) is responsible for managing and optimizing database systems, ensuring data availability, performance, and compliance, while also developing automated solutions and collaborating with site customers.

Join Micron as a Data Scientist to apply data analysis and machine learning expertise toward innovations in memory, storage, AI, and related products. The posting emphasizes company mission, inclusion, and application process; specific role responsibilities and qualifications are not listed.

Manage, maintain, and optimize critical database systems (SQL Server, Oracle). Perform installs, patches, backups, access control, monitoring, performance tuning, incident/change/problem management, on-call support, capacity planning, automation, reporting, and collaborate with global teams and site stakeholders.

Analyze semiconductor manufacturing data to identify yield detractors, build data pipelines, apply statistical and machine learning models for yield improvement, support DOE experiments, and create automated dashboards and reports for engineering stakeholders.

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.

Lead end-to-end delivery of enterprise data and analytics solutions for retail banking, spanning requirements analysis, solution design, testing, implementation, governance, and production support for ML/NLP-enabled and rule-based decisioning systems.

Lead large-scale analytic projects focusing on driving business impact through strategic analysis and machine learning/NLP models in the financial sector.

Lead techno-functional data solutions for Wholesale Banking, Retail, and Global Markets. Gather requirements, design/validate data models (Teradata FSDM, dimensional), profile and map data from core systems, author specifications, collaborate with architects and engineers, execute data validation tests, and provide data-driven insights for strategic decisions.

Lead enterprise data product and Data-as-a-Service strategy, delivery, operationalization, governance, and adoption within a regulated banking environment. Build reusable, governed data products supporting analytics, AI, reporting, and business decision-making. Establish operating models, metadata and lineage standards, quality controls, self-service consumption channels, and Centre of Excellence practices. Partner with senior stakeholders, engineering, technology, governance, risk, and business teams to prioritize use cases, manage delivery, enable AI context, and track measurable value.

Design and build enterprise-scale ingestion frameworks and Lakehouse platforms. Develop reusable Spark/Python components, data models, data products, quality controls, and observability. Support CI/CD, DevOps, Kubernetes-based deployments, and operationalize ML models for AI-driven analytics and risk/fraud use cases in banking.

Lead regional AFC/AML analytics capability: design, build, validate and deploy AML/fraud detection models; guide country analytics teams; ensure model governance, data suitability, and production readiness across stakeholders and regions.

Lead functional business analysis for enterprise KYC/AML capabilities across Group Technology & Ops. Manage stakeholder relationships, define capabilities, translate business requirements into functional and non-functional requirements, oversee testing and scope control, support deployments and post-go-live knowledge transfer, and lead and develop a team of functional business analysts.

Lead the development and deployment of Generative AI applications, collaborate with teams to deliver business use cases, and enhance data science skills.

Leads Binance’s market data function for its Traditional Finance expansion. Owns strategy, sourcing, licensing, vendor negotiations, integration, governance, entitlements, data quality, lineage, compliance, and budget management. Partners with product, engineering, legal, finance, exchanges, and vendors to build scalable market data infrastructure. Builds and mentors a team while serving as the internal expert on market data and market structure.

Develop and deploy machine learning models that improve e-commerce search relevance, recommendations, product classification, and customer intent understanding. Responsibilities include collecting and preparing large datasets, feature engineering, predictive modeling, validation, testing, and leading work in areas such as NLP, computer vision, information retrieval, and deep learning. The role requires building scalable, reusable machine learning pipelines and collaborating across concurrent projects.

The Technical Product Manager is responsible for product outcomes across the lifecycle, involving product vision, stakeholder engagement, backlog prioritization, and technical leadership.

The Enterprise BD Manager will expand and maintain key enterprise accounts, driving growth through strategic relationships and complex solution structures in maritime risk and sustainability.

The Transformation Lead for AI in APAC will manage change and drive adoption of AI tools across the studios, engage stakeholders, and run training programs.

Wayvia is building a talent pipeline for engineering roles focusing on backend systems, data pipelines, and product engineering. They seek curious and collaborative engineers to connect with and solve future challenges.