Senior ML Quant Engineer

Paragon Alpha - Hedge Fund Talent Business; Greater London; On-site; GBP 150,000 - 210,000; Full time

Job summary

Paragon Alpha is seeking a Senior Quant Engineer to elevate the build of a systematic trading platform in London. You will design and maintain data ingestion pipelines, feature generation, backtesting, and experiment tracking, turning research into production-ready systems with robust execution workflows.

You will validate data and models, guard against look‑ahead bias and overfitting, and apply machine learning to develop predictive signals with careful controls for leakage.

Qualifications

• Proven ability to build robust production data pipelines and research infrastructure.
• Strong Python and ML fundamentals with a focus on reproducibility.
• Experience with data validation, backtesting, and experiment tracking.

Responsibilities

• Build and maintain research infrastructure for data ingestion, feature generation, backtesting, and experiment tracking.
• Turn strategies into production systems with signal logic, portfolio construction, and execution workflows.
• Validate data and models, checking for look‑ahead bias, overfitting, and realistic trading costs.
• Apply machine learning to build and evaluate predictive signals with controls for data leakage.
• Monitor live strategies, track performance, risk, and system health, and respond to anomalies.
• Improve reliability and speed of pipelines to enable reproducible trading research and deployment.

Skills

Python

Machine Learning

Pandas

SQL

Airflow

AWS

FastAPI

Job description

Paragon are representing a world class systematic trading house, working with an MF equity team.

We have been mandated to find an elite Senior Quant Engineer to take the build to the next level.

Focus:

• Build and maintain research infrastructure for data ingestion, feature generation, backtesting, and experiment tracking.
• Turn strategies into production systems, implementing signal logic, portfolio construction, and execution workflows.
• Validate data and models, checking for errors, look-ahead bias, overfitting, and realistic trading costs.
• Apply machine learning to build and evaluate predictive signals, with careful controls for overfitting and data leakage.
• Monitor live strategies, tracking performance, risk, system health, and unexpected behavior.
• Improve reliability and speed, optimizing code and making trading and research pipelines reproducible and robust.

Stack: Python, Machine Learning, FastAPI, Pandas, SQL, Airflow, AWS