Top Data Jobs in Singapore

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.

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.

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.

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 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.

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.

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.

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.

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.

Design, build, and manage large-scale search and content recommendation systems using machine learning and personalization techniques. Collaborate with content, product, and business teams to define success metrics, drive AI product improvements, and maximize content value and user impact.

Lead a data insights team to deliver analytics, AI and automation solutions that drive measurable business outcomes. Partner with stakeholders to translate problems into analytical initiatives, manage end-to-end analytics lifecycle, operationalize AI-enabled capabilities, establish KPIs and reporting, and mentor data talent to build organizational analytics maturity.

Lead advanced analytics and ML projects: perform EDA, feature engineering, statistical modeling, and build/optimize production ML and Generative AI solutions (LLMs, embeddings, RAG). Collaborate with engineering and MLOps to deploy and monitor models, create visualizations and dashboards, translate insights for stakeholders, and mentor junior team members.

Partner with customers in APJ to design, run, and analyze experiments; advise on statistical methodology, measurement frameworks, and experimentation architecture; create enablement materials; collaborate with Sales, SE, Product, and Engineering to scale experimentation programs and influence product direction.

The Associate Director will lead engineering strategy, oversee multiple teams, ensure quality delivery of applications, and promote a high-performing, innovative engineering culture.

Lead development and evolution of the Group's AI risk framework, translate policy into practical controls, engage business and tech teams, monitor AI use cases, guide resolution of control gaps, and align frameworks with regulatory expectations and industry developments.

Support and coordinate GenAI training and advocacy across the bank, run workshops, clinics and hackathons, analyze and report on program metrics, develop learning content, collaborate cross-functionally, evangelize innovation, and coordinate internal festivals and campaigns to boost adoption and acumen.