AlgoQuant - Senior Quant Researcher
Requirements
• ● Exceptional quantitative background — PhD or equivalent research depth in machine • learning, statistics, physics, mathematics, or computer science • ● Genuine expertise in modern ML and DL: transformers, attention mechanisms, graph • neural networks, boosting algorithms (XGBoost, LightGBM), and reinforcement learning — • not just familiarity, but hands-on implementation experience • ● A track record of applying ML in a live, capital-at-risk environment — attributable P&L or • measurable out-of-sample performance from systematic strategies • ● Rigorous, almost paranoid approach to model validation — deeply experienced with the • failure modes of ML in finance: overfitting, regime change, feature leakage, and • non-stationarity • ● Strong programming skills — Python required; C++ or Rust a strong plus for production • ● Experience working with large, complex, or unconventional datasets; on-chain data • ● Self-directed and high-agency — you set your own research agenda and drive it to • ● Crypto market exposure a strong plus; intellectual curiosity about digital asset market • structure essential
Responsibilities
• ● Design and deploy advanced ML and DL models for alpha signal generation across digitalasset markets● Work across the full model stack: feature engineering, architecture selection, training andvalidation regimes, and live signal monitoring● Apply and adapt state-of-the-art techniques — transformer architectures, graph neuralnetworks, reinforcement learning, and ensemble methods — to financial predictionproblems● Build robust, production-grade research pipelines with a rigorous approach to preventinglookahead bias, data leakage, and overfitting● Analyse microstructure, order flow, and cross-venue dynamics to enrich feature sets andimprove signal quality● Collaborate with engineers to move models from research to production infrastructure● Mentor junior researchers and raise the bar for statistical rigour across the team● Contribute to shared research infrastructure, tooling, and datasetsWhat we are looking for● Exceptional quantitative background — PhD or equivalent research depth in machinelearning, statistics, physics, mathematics, or computer science● Genuine expertise in modern ML and DL: transformers, attention mechanisms, graphneural networks, boosting algorithms (XGBoost, LightGBM), and reinforcement learning —not just familiarity, but hands-on implementation experience● A track record of applying ML in a live, capital-at-risk environment — attributable P&L ormeasurable out-of-sample performance from systematic strategies● Rigorous, almost paranoid approach to model validation — deeply experienced with thefailure modes of ML in finance: overfitting, regime change, feature leakage, andnon-stationarity● Strong programming skills — Python required; C++ or Rust a strong plus for productionperformance● Experience working with large, complex, or unconventional datasets; on-chain dataexperience a plus● Self-directed and high-agency — you set your own research agenda and drive it tocompletion● Crypto market exposure a strong plus; intellectual curiosity about digital asset marketstructure essential
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