Skills

Python SQL NoSQL Big Data Data Engineering Risk Management Decision-making Architecture Machine Learning PyTorch Scikit-Learn TensorFlow Deep Learning Databases Azure AWS cloud platforms Analytics GCP Spark

Job Specifications

This temporary role requires an ML Quant Engineer with expertise within an Investment Bank. The position is based in London and involves developing and implementing machine learning models to support financial decision-making.
Client Details
The hiring organisation is a large entity within the financial services industry.
Description
Design and implement machine learning models for financial applications, with a focus on derivatives pricing, risk analytics, and market forecasting.
Build scalable ML pipelines to process large volumes of financial data efficiently.
Develop deep learning architectures for time series prediction, anomaly detection, and pattern recognition in market data.
Optimise model performance using techniques such as hyper-parameter tuning, ensemble methods, and neural architecture search.
Collaborate with quantitative analysts to align ML models with pricing methodologies and identify opportunities for innovation.
Support the deployment of ML solutions into production systems for Real Time risk management and pricing automation.
Profile
Advanced Machine Learning Expertise - Demonstrates deep understanding of ML algorithms (supervised, unsupervised, reinforcement learning) and has hands-on experience with deep learning architectures like RNNs, LSTMs, and Transformers.
Strong Financial Domain Knowledge - Understands financial instruments, derivatives, and risk management principles, with experience applying ML in trading, pricing, or risk analytics contexts.
Technical Proficiency - Expert in Python and familiar with ML frameworks such as PyTorch, TensorFlow, and JAX. Skilled in using tools like scikit-learn, XGBoost, and LightGBM.
Data Engineering & Infrastructure Skills - Comfortable working with big data technologies (Spark, Dask), SQL/NoSQL databases, and cloud platforms (AWS, GCP, Azure). Able to build scalable ML pipelines for large-scale financial data.
Model Optimisation & Deployment Experience - Proven track record of deploying ML models at scale, with experience in hyper-parameter tuning, ensemble methods, and neural architecture search.
Collaborative & Business-Focused - Works effectively with quants and stakeholders to translate financial requirements into ML solutions. Communicates insights clearly and aligns models with strategic business goals.
Innovative & Analytical Mindset - Capable of developing data-driven approaches that complement traditional quantitative models and drive measurable impact in pricing and risk analytics.
Job Offer
A competitive daily rate up to PS1200 per day (inside IR35), depending on experience.
The opportunity to work on cutting-edge machine learning projects in the financial services industry.
A temporary role offering valuable exposure to a global organisation in London.
BASED 4 DAYS PER WEEK IN THE OFFICE (Central London)

About the Company

Welcome to the Michael Page global company profile. Michael Page has five decades of expertise in professional services recruitment. We were established in London in 1976, and over this period we've grown organically to become one of the best-known and most respected consultancies, with an office network spanning six continents. While size has its advantages, it doesn't define us - the nature of our organic growth means that each new office is integrated into the region that it serves. It also means that as an employer lo... Know more