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Quantitative Researcher

Grasshopper
CompanyGrasshopper
CategoryScience & Research
LocationSingapore
RemoteOn-site (inferred)
EmploymentNot stated
LevelNot stated
SalaryNot stated by the employer
Posted19 Feb 2026
Last verified30 Jul 2026
SourceEmployer career page (greenhouse)
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Description
About Grasshopper Grasshopper is a quantitative trading technology provider based in Singapore, and is the holding company of Grasshopper Asset Management. Our state-of-the-art technology, built from the ground up in-house, puts us at the forefront of developments in electronic trading. An unbroken record of consistency and profitability is underpinned by firm values of curiosity, empowerment and flexibility. About Grasshopper Asset Management Grasshopper Asset Management, a subsidiary of Grasshopper, is a Licensed Fund Management Company (LFMC) regulated by the Monetary Authority of Singapore. With low-latency quantitative strategies, we generate diversified, stable and uncorrelated returns with an unbroken 18 year streak of profitability by maintaining multiple layers of algorithmic and manual risk controls. About the Role: We are seeking a highly motivated Quant Researcher to be part of a new team. You will help design, analyse and refine statistical systematic trading strategies in a fast-paced, data-driven environment. This is a career-defining opportunity which will allow you to gain hands-on exposure to real trading systems, market microstructure, and quantitative research workflows whilst being one of the founding members of a new team. As a key member of the Trading Team, you’ll: Research and improve existing systematic trading strategies across global equities, futures, commodities, or options Analyse large datasets of market microstructure, order book, and tick data to identify opportunities Develop, backtest, and optimise predictive models using modern statistical, econometric, and ML techniques Develop production-quality code to translate research insights into live trading systems Evaluate performance, quantitative risk management, and continuously refine existing strategies through data-driven iteration Contribute to the research infrastructure — simulation tools, data pipelines, and performance analytics We’d love for you to have: 2-5 years of programming experience in Python (pandas, numpy, scipy), or statistical/machine learning tech-stack in another programming language that we can reuse/redirect you in Python/C++ Solid understanding of data structures, algorithms, and software fundamentals; demonstrable ability to turn quant ideas into working codes Familiarity with predictive statistics, machine learning, econometrics or time-series analysis, along with an interest in scientific methods for truth finding/hypothesis testing Bilingual English and Mandarin (spoken and written) competency to liaise with external stakeholders Basic knowledge of networking and Linux environments is a plus Basic knowledge of producing reproducible research is a plus Nice to haves: Balance of pragmatism with a desire to see things done right and ambition to see new teams succeed and profit-share Open-minded, able to propose ideas, and provide constructive feedback on others' ideas Ability to reason under uncertainty, form hypotheses, test them empirically, and accept failure/post mortem Strong attention to detail and curiosity about how markets work What we offer: 21 days annual leave An opportunity to learn from experienced professionals, fostering mentorship opportunities and personal growth Comprehensive Insurance Package with extended coverage for dependents Well stocked pantry Annual Dental & Wellness budget Gym membership Employee bonus referrals #gam What you can expect working at Grasshopper: At Grasshopper, you will be working in a diverse and dynamic environment with a flat hierarchy. With over 100 employees and 15 nationalities working in an open office, communication is essential to performance. To keep our edge as the “small giant” of trading technology, we give employees a high level of autonomy and encourage them to get creative, take risks, make mistakes and learn from them. The sprint is on! Grass
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