Top Research Jobs in Singapore

Quantitative Researchers work on developing mathematical models, backtesting trading strategies, and conducting statistical analysis, utilizing complex data sets and algorithms.

As a Quantitative Research Analyst Intern, you will develop and improve trading models, conduct statistical analysis, and collaborate with team members in a data-driven environment.

The role involves developing and improving mathematical models for trading, analyzing statistical data, backtesting trading models, and using unconventional data for insights.

The Marketing Manager focuses on planning and executing omnichannel marketing campaigns to enhance awareness and engagement of Morningstar's research across global audiences, while managing multiple campaigns and collaborating with various teams.

Design, develop, and deploy software solutions for automated trading systems while collaborating with researchers on technology priorities.

The role involves researching and developing large language models (LLMs) with a focus on transformer architecture, data curation, distributed training, and optimization. Responsibilities include conducting experiments, collaborating with teams, and staying updated on deep learning advancements.

The role involves pioneering machine learning techniques, developing advanced AI models, building data pipelines, and translating prototypes into products in code analysis.

Lead user research and market insights to evaluate customer perception of Airwallex. Design qualitative and quantitative studies, synthesize feedback, present data-driven recommendations to leadership, and collaborate with product, engineering, and commercial teams to integrate insights and improve customer experience.

The intern will develop trading strategies, back test models, analyze data, and collaborate with the research team to innovate and refine monetization systems.

Conduct research, statistical analyses, and develop automated trading strategies using large data sets and sophisticated statistical techniques.

The Quantitative Research Analyst will develop and test trading strategies, implement models, and conduct statistical analysis in financial markets.

Conduct research for investment opportunities, evaluate viability and performance of investments, and prepare financial models and recommendations.

The role involves developing and teaching courses in Design & Technology Education, supervising students, and conducting impactful research in education.

Conduct research and technical evaluations of AI models (especially generative AI), build feasibility studies and proofs-of-concept, mentor and up-skill colleagues, participate in communities of practice, and keep current with AI research to de-risk deployments and advance company AI capabilities.

Conduct in-depth research and due diligence on crypto projects, produce comprehensive reports and risk assessments, analyze on-chain and internal data, evaluate TradFi intersections with tokenization, engage with project teams, and monitor market trends to inform Binance Research and cross-functional stakeholders.

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.

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.

Source and evaluate blockchain and crypto projects for listing, build relationships with founders and VCs, support due diligence and tokenomics analysis, drive internal approvals, negotiate terms, and represent Binance at global events.

Early-career research role designing and running experiments on LLM reasoning, post-training alignment, and test-time scaling. Implement model variants and training pipelines in PyTorch/Hugging Face, evaluate on crypto-native data, maintain experiment tracking (W&B), and partner with engineering to translate research to production.

Manage post-listing research, incident response, and operations for listed tokens. Conduct quantitative and qualitative token reviews, identify and mitigate risks, onboard listings, liaise with projects, and improve SOPs. Produce concise risk reports and support major incidents.

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.

Lead long-horizon R&D to define future decision intelligence: explore reasoning architectures, prototype and formalize system primitives, evaluate new signal classes, and translate research into reusable components aligned to strategic product direction.

Apply formal decision theory and structured reasoning to enterprise problems: design decision frameworks, model uncertainty and tradeoffs, test decision architectures, run experiments, and translate research into system-ready primitives collaborating with engineering and product teams.

Build and maintain experimental decision-system prototypes, evaluation tooling, simulation environments, and research infrastructure to enable rapid iteration and validation of research concepts in real-world constraints.

Lead and conduct experimental research to develop and characterise solid-state quantum sensors (e.g., color centres in diamond). Design and set up laboratory instrumentation, develop experimental protocols, lead sub-projects, document experiments, mentor lab staff and students, maintain lab operations, and support publications, proposals, and reporting.