Lead of Modeling Deputy to Head of ML

We are seeking a senior leader to own the Model Layer of our ML-driven quantitative research platform. This role leads architecture design model development validation lifecycle management and standards for all ML models powering our signal generation pipeline. You will work closely with Quant Research Feature Engineering Data Engineering Trading and AlgoDev to deliver robust production-grade predictive models.

What Youll 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.

Qualifications :

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

Additional Information :

Nice to Have

• Experience in MFT/HFT environments (intra-day).

• Publications in ML or quantitative finance.

• Contributions to open-source ML projects.

Remote Work :

Yes

Employment Type :

Full-time