Top Data Jobs in Singapore

Design, build, and maintain Microsoft Fabric data architecture and ETL pipelines (Fabric, Synapse, ADF). Transform and optimize data for analytics and AI, support RAG/LLM workflows, ensure data quality, governance, and security, monitor performance, and document processes.

Analyze casino operations and product performance, build models and ML solutions for floor optimization, demand forecasting and manpower scheduling, create dashboards and A/B tests, collaborate with Operations, Marketing, BI, and IT to translate analytics into business strategy and operational improvements.

Develop and implement computer vision algorithms, evaluate model performance, and integrate solutions into larger systems, while maintaining documentation and staying updated on technology advances.

The Senior Data Scientist will develop AI solutions for gamers, focusing on Reinforcement Learning and Generative AI, collaborating closely with game developers and engineers.

The Senior Data Engineer will lead AI Data Engineering initiatives, architect and manage data infrastructure, and optimize data pipelines for AI and ML applications using various tools and technologies.

The Product Engineer will design and implement data-driven and edge-intelligent solutions focusing on real-time insights, Edge AI systems, and scalable data pipelines to improve service productivity.

Manage full lifecycle of data centre projects; oversee planning, execution, electrical infrastructure management, vendor coordination, risk assessment, and communication with stakeholders.

Responsible for planning, scheduling, and monitoring Data Center projects, ensuring timely delivery and coordination with stakeholders.

The Consultant/Senior Consultant will manage projects, develop practice-related content, and enhance data delivery methodologies, ensuring timely and quality project completion.

The role involves leading data governance and finance transformation projects, supporting client initiatives, and implementing enterprise data strategies for financial institutions.

Data Scientist II role focused on building AI/ML models for optimization. Responsibilities include data analysis, model training, and collaboration across teams to implement AI/ML solutions.

The Data Engineering Lead oversees data engineering operations, manages teams, ensures data integrity, and drives innovation in data management and governance.

This internship involves developing data processing platforms to support semiconductor and display manufacturing. Interns will engage in hands-on work within a global team to drive innovation.

Lead system and static data setups for new client and fund onboarding, manage conversion activities, validate NAV and shareholder data, standardize processes, liaise with stakeholders, and ensure ISAE/SOC compliance while guiding the client data team.

Design, build, test and support batch and streaming data pipelines and curated datasets on a modern Lakehouse and AI data platform. Implement data modelling, schema evolution, partitioning and data quality controls. Partner with platform teams and consumers, contribute tooling and standards, and lead delivery for workstreams while supporting junior engineers.

Support Singapore laboratory operations by developing and maintaining digital tools (Power BI/Power Apps), performing data analysis to improve equipment calibration, maintenance, and inventory processes. Manage end-to-end inventory activities, procurement, receiving, storage, archival and disposal of samples/consumables. Ensure 5S, safety and cGMP compliance, coordinate vendor logistics and present findings to Quality leadership to drive quality and productivity improvements.

Lead network capacity planning and analysis across a large global infrastructure. Analyze traffic and performance metrics, forecast growth, recommend hardware upgrades, produce capacity reports and KPIs, mitigate congestion risks, and collaborate with network, demand management, and project teams to align capacity plans with business goals.

Support distribution & logistics team with data collection, analysis, and audits. Create templates, work with external partners, benchmark performance, audit freight documentation, assist monthly customs compliance reporting, generate management reports, and support SOP and project implementation.

Lead R&D of AI/ML algorithms for multimodal medical imaging, digital pathology, spatial-omics and molecular data to discover deployable biomarker solutions. Collaborate cross-functionally, build reusable ML pipelines, partner with external institutions, present findings, and author technical proposals and reports to support drug development.

Lead R&D of AI/ML algorithms for multimodal medical imaging, digital pathology, spatial-omics and molecular data to discover deployable biomarker solutions, build reusable ML pipelines, collaborate across teams and partners, and present and publish results to influence drug development decisions.

Two-year Talent Accelerator for Senior-level scientists to integrate biology, data, and AI in translational drug discovery. Work in multidisciplinary teams to develop translational models, spatial omics and imaging assays, AI/ML pipelines, and multimodal data integration to identify biomarkers, inform target decisions, and communicate findings to stakeholders.

Lead delivery and iteration of DeFi/on-chain data features for a Web3 wallet: define requirements, manage protocol integrations and multi-chain expansion, align data standards across teams, build metrics and feedback loops, and ensure data accuracy, stability, and quality.

Design and build high-performance Java microservices and data pipelines to parse, transform, and analyze multi-chain on-chain data in real time. Integrate with Kafka, Redis, RDS, and data lake systems; optimize low-latency computation for token prices, swaps, liquidity metrics; ensure fault tolerance, observability, and alignment with long-term data platform strategy.

Early-career Data Scientist role in Binance Accelerator Program focused on designing and evaluating AI models to detect P2P scams. Responsibilities include developing CV-based document verification, LLM-based detection and interpretable explanations, image-embedding feature extraction, and collaborating with engineers to optimize detection pipelines and deliver insights.

Build and maintain user tagging and profiling systems, analyze large-scale behavior data, perform feature engineering and exploratory analysis, support cross-functional teams for segmentation, monitor tag effectiveness, detect anomalies and fraud patterns, and improve data methodologies to enable risk control and personalized services.