Quantitative Researcher

7 days ago

Singapore DELVEGA CAPITAL PTE. LTD. Full time

Role Overview As a Junior Quant Researcher at Delvega, you will work directly with the core HFT and derivatives research team to design, test, and refine systematic trading strategies. You will contribute to research across options , stat-arb , and HFT microstructure , helping to convert research hypotheses into deployable trading logic. This is a hands‐on role with real responsibility, exposure to live trading, and room for fast growth. Key Responsibilities Assist in researching, designing, and backtesting strategies within: High-frequency trading (HFT)Options pricing, volatility surfaces, skew/smile analysis Statistical arbitrage & mean-reversion models Build and maintain research tools, simulation frameworks, and model evaluation pipelines. Work with large, tick-level datasets including order-book feeds and derivatives market data. Implement research in Python and help translate production-critical components to C++in coordination with engineering. Run experiments, analyse model behaviour, and iterate based on performance metrics. Collaborate with senior quants on idea generation, risk control, and model validation. Document research outcomes and maintain reproducible research workflows. Required Skills & Qualifications Bachelor's / Master's in: Computer Science, Engineering, Mathematics, Physics, Statistics, Quantitative Finance , or related fields. Strong programming skills in: Python (NumPy, Pandas, SciPy)C++(good grasp of syntax, OOP, STL; interest in learning low-latency concepts)Solid understanding of: Probability, statistics, time-series analysis Linear algebra and optimization Curiosity about markets — derivatives, options, microstructure, or arbitrage. Ability to handle and analyse large datasets. Comfort with Linux, Git, and command-line workflows. Preferred / Bonus Skills Experience with options Greeks, implied volatility, or volatility modelling. Understanding of limit order books, queue dynamics, or market microstructure. Prior work on stat‐arb signals, PCA-based models, factor models, or mean‐reversion research. Exposure to HFT concepts: order-types, tick‐to‐trade workflows, latency fundamentals. Experience with backtesting frameworks. Knowledge of machine learning techniques (not mandatory). #J-18808-Ljbffr

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