Full-Time Lead of Modeling / Deputy to Head of ML

What You’ll Do

• Lead the design and evolution of the Model Architecture Portfolio across boosting models, time-series deep learning, GNNs, and advanced architectures such as DeepLOB/DeepOB.
• Build and maintain leakage-free training pipelines, including IS/OOS splits, walk-forward and rolling validation, and high-quality target engineering.
• Define validation protocols (IC/Rank IC, decay, stability) and conduct statistical robustness testing.
• Develop explainability and diagnostics frameworks using SHAP, permutation methods, and feature contribution analysis.
• Architect ensemble strategies (stacking, blending, regime-switching) and manage routing logic across signals and regimes.
• Own monitoring, drift detection, retraining schedules, and overall model lifecycle governance.
• Lead and mentor a team of ML researchers and modeling engineers; establish standards for modeling quality, experimentation, and documentation.
• Partner cross-functionally to ensure seamless integration of models into production trading systems.

Must-Have Experience

• 7+ years in machine learning, including 3+ years in quantitative finance or financial ML.
• Deep knowledge of ML models.
• Strong statistical background (bootstrap, t-tests, serial correlation, heteroskedasticity).
• Experience building real-time or near-real-time ML systems and pipelines.
• Strong understanding of signal validation (IC, Rank IC, decay, cross-sectional behavior).
• Solid engineering skills in Python, PyTorch/TF, NumPy, Pandas.

Quant & Market Knowledge

• Familiarity with market microstructure, order book data, and factor exposures.
• Understanding of PnL decomposition, execution effects, and slippage dynamics.

Leadership & Communication

• Experience leading technical teams and driving modeling strategy.
• Strong communication, documentation, and cross-functional collaboration skills.

Impact & Scope

This is a high-impact leadership role overseeing critical components of our ML Factory. You will shape modeling strategy, standards, and architecture across the entire research and production pipeline

Nice to Have

• Experience in MFT/HFT environments (intra-day).
• Publications in ML or quantitative finance.
• Contributions to open-source ML projects.

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