Senior Quant Researcher

We are engaged by one of the most successful quantitative trading firms globally to identify a Senior Quantitative Researcher to join its systematic trading team. The firm deploys large-scale capital across global markets, leveraging statistical, econometric, and machine-learning-based models to generate alpha across asset classes.This is a front-office research mandate, offering direct impact on live trading strategies and access to extensive data infrastructure, HPC clusters, and cross-asset datasets.Ideal Background5–12 years’ experience at a Tier-1 quant platform Demonstrable PnL contribution or model ownership within a production trading environment.Ability to bridge research, implementation, and execution seamlessly.Evidence of strong publication, Kaggle ranking, or open-source contribution is a plus.Alpha Research & Strategy Development:Design, test, and implement short- to medium-term alpha models in equities, futures, FX, or digital assets.Leverage econometric, statistical, and ML frameworks to identify predictive signals and inefficiencies.Backtesting & Simulation:Build robust research pipelines with rigorous validation (cross-validation, out-of-sample tests, walk-forward analysis).Evaluate model stability, signal decay, and execution impact under various market regimes.Collaboration with PMs / Tech:Partner with portfolio managers and developers to translate signals into executable strategies.Work closely with engineers to optimize compute resources and data handling.Data Science & Engineering:Source, cleanse, and structure large alternative and market datasets (tick-level, news, sentiment, etc.).Build high-performance data ingestion and feature-engineering pipelines.Continuous Improvement:Conduct post-trade analysis to measure drift, slippage, and performance attribution.Publish internal research notes and mentor junior quants in model governance and reproducibility.Education: PhD or MSc in Mathematics, Physics, Computer Science, Statistics, Financial Engineering, or related quantitative field from a top-tier institution (e.g., MIT, Stanford, Cambridge, Oxford, ETH, Tsinghua).Programming: Exceptional proficiency in Python and C++ (or C#/Java), with strong numerical libraries (NumPy, pandas, PyTorch, TensorFlow, scikit-learn).Statistics & ML: Deep understanding of probability, time-series modelling, regression, stochastic calculus, and ML methods (GBM, XGBoost, random forests, reinforcement learning).Data Handling: Experience with large-scale datasets, distributed systems (Spark, Hadoop, Dask), and advanced SQL or kdb+/q.Financial Domain Knowledge: Proven experience designing alpha signals, risk models, or execution models in a systematic or market-making context.Expected Research Impact: α (annualized) ≥ 5% with SR ≥ 2.0 over 3-year rolling window; model turnover < 0.5x/day; drawdown < 3%.