Quant Researcher: ML‑Driven Finance & AI

NCSL International; Greater London; Hybrid; GBP 90,000 - 140,000; Full time

Job summary

NCSL International is seeking a highly capable Machine Learning Engineer (Quant) to advance predictive models for financial markets in a production setting. You will blend ML research with practical data infrastructure and AI engineering to deliver insights used by portfolio managers.

The role emphasizes rigorous validation, explainability, and collaboration with researchers, data scientists, and investment professionals in a fast-paced environment.

Qualifications

• Advanced degree or equivalent practical experience in a quantitative field.
• Strong foundation in statistics and machine learning with publication or project experience.

Responsibilities

• Design, develop, and deploy ML models for prediction problems across financial markets.
• Build and evaluate models for issuer credit deterioration, costs, liquidity, and relative value.
• Develop NLP and LLM-based pipelines to process financial documents.
• Create data pipelines and production-ready ML systems for live research environments.
• Provide robust validation, explainability, and documentation for governance.

Skills

Gradient boosting

Tree-based ensembles

Regularised regression

Supervised learning

Statistical learning theory

Predictive modelling

Feature engineering

Model selection & validation

Probability forecasting

Calibration techniques

Uncertainty quantification

Explainability methods

Imbalanced datasets handling

Missing data treatment

Time-series modelling

Forecasting methodologies

Education

MSc or PhD in ML / Statistics / Math / CS / Econometrics

Tools

Python

SQL

Git

Job description

NCSL International is seeking a highly capable Machine Learning Engineer (Quant) to advance predictive models for financial markets in a production setting. You will blend ML research with practical data infrastructure and AI engineering to deliver insights used by portfolio managers.

The role emphasizes rigorous validation, explainability, and collaboration with researchers, data scientists, and investment professionals in a fast-paced environment.