Quantitative Researcher

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 empi...