Lead of Modeling / Deputy to Head of ML

BHFT; Dubai; On-site; AED 440,000 - 551,000; Full time

Job summary

A financial technology firm in Dubai seeks a senior leader to drive the Model Layer of their ML-driven quantitative research platform. This role requires robust experience in machine learning and quantitative finance, overseeing architecture design and model development. The ideal candidate will lead a team of ML researchers, build predictive models, and ensure seamless integration with trading systems. A strong statistical background and proficiency in tools like Python and PyTorch are essential, along with excellent leadership and communication skills.

Qualifications

• 7+ years in machine learning, including 3+ years in quantitative finance or financial ML.
• Strong statistical background (bootstrap, t-tests, serial correlation, heteroskedasticity).
• Experience building real-time or near-real-time ML systems and pipelines.

Responsibilities

• Lead the design and evolution of the Model Architecture Portfolio.
• Build and maintain leakage-free training pipelines.
• Define validation protocols and conduct statistical robustness testing.
• Develop explainability and diagnostics frameworks.
• Own monitoring, drift detection, and overall model lifecycle governance.

Skills

Machine learning

Quantitative finance

Statistical analysis

Python programming

Communication skills

Tools

PyTorch

TensorFlow

NumPy

Pandas

Job description

Job Description

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 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.

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.