Top Data Jobs in Singapore

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.

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.

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.

Develop, deploy, and maintain LLM pipelines and RAG QA/search systems; design and optimize prompts and multi-agent LLM architectures; operate multi‑GPU/cluster inference; build evaluation pipelines for model quality, bias, and hallucination; collaborate with product and CS teams to integrate conversational AI.

Conduct original research on generative AI and LLMs for crypto/finance, design and run rigorous experiments, build evaluation frameworks, develop post-training and test-time scaling methods, and collaborate with engineers to deploy research into production focused on agent reasoning, alignment, and robustness.

Lead Compliance Data Analytics to research and analyze risk patterns (fraud, account takeover, payment fraud), deploy and iterate rules/models, partner with stakeholders to balance user experience and risk mitigation, drive business-centric analytics and strategic metrics, and build and manage a high-performing compliance analytics team.

Lead a Data Analyst team to analyze PB-scale transactional and customer data, build algorithmic systems and visualizations, implement ML/LLM solutions for compliance use cases, support business stakeholders with data requests, and translate findings into actionable recommendations.

Design, develop, and maintain high-performance, highly available backend services and APIs for Big Data products using Java/Python. Manage full service lifecycle, capacity planning, profiling/benchmarking, troubleshooting, security, and system architecture. Contribute to open-source big data projects and build scalable tools for global operations.

Lead end-to-end LLM pipeline for customer service scheduling: data prep, prompt design, RAG systems, multi-agent architectures, multi-GPU deployment, evaluation pipelines, and chatbot integration to improve model quality and decision-making.

Early-career research role implementing and running experiments on LLM reasoning, post-training alignment, and agentic AI for crypto markets. Tasks include literature synthesis, model implementation (PyTorch/Hugging Face), experiment tracking (Weights & Biases), and collaborating with engineers to translate research into production.

Early-career role analyzing PB-scale on-chain, transactional and customer data. Perform data engineering, ETL, querying, build visualizations and ML models, translate insights for teams, manage reporting environment, secure confidential data, and troubleshoot reports/databases.

Six-month junior fixed-term role to design and develop LLM-based agents for Web3 scenarios, build token research and summarization agents, assist in trading agent development using market and on-chain data, and experiment with prompt engineering, memory, and multi-agent orchestration and evaluation.

Design and build high-throughput reconciliation systems and data pipelines. Implement real-time (Flink) and batch (Hive) workflows, write complex SQL for validation and anomaly detection, collaborate with Finance/Risk/Product on reconciliation rules, optimize for low latency, and leverage AI/LLM tooling to automate triage and improve operations.

Early-career data scientist role to analyze user and content data, build metrics, design recommendation strategies (recall, ranking, cold-start, diversity), run A/B tests, evaluate experiments, explore LLM/multimodal applications, and improve data pipelines and feature logging.

Build and apply NLU/NLP methods (sentiment, intent, NER) to financial text streams, design ML models and time-series analyses, backtest and optimize quantitative trading strategies, and collaborate with trading teams to manage risk and improve model performance.

Early-career role building and evaluating LLM-powered agentic trading systems for crypto markets. Develop pipelines, prompt strategies, retrieval, model evaluations, and ship Python code for production features.

Lead design and implementation of scalable, high-performance backend services for traditional and AI-powered search. Own search architecture, optimize indexing/retrieval/ranking, build reusable frameworks, collaborate with data scientists on LLM-enabled search and agents, and deliver robust, low-latency solutions.

Conduct research and development on LLM technologies (RAG, prompt engineering, knowledge-grounded dialogue). Improve foundation model performance via data acquisition, evaluation, SFT, reward modeling, and RL. Prototype downstream AI/agent products for Binance and track academic and industry advances.

Design, develop, and manage large-scale search and content recommendation systems. Apply ML-based personalization to improve content matching and distribution. Lead core recommendation module development, define success metrics, and collaborate with content, product, and business teams to drive AI product upgrades and better user and business outcomes.

Design and develop high-performance backend services for an AI-powered trading assistant: build AI agent execution, trading order execution, AI sub-account and API key lifecycle management, integrate Binance trading APIs, implement skills/tool-calling frameworks, and ensure scalable, low-latency real-time data pipelines, observability, and security.

Develop and optimize marketing-focused algorithmic models (CTR/CVR, LTV, attribution, uplift), design lifecycle strategies for acquisition/retention, validate via A/B testing and causal inference, and collaborate with data/product/operations to deploy models into marketing systems to drive growth.