Machine Learning Quant Engineer

Machine Learning Quant Engineer

Posted 3 days ago by Michael Page

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£1,200 Per day
Inside
Onsite
City Of London, England, United Kingdom
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Algorithms Anomaly Detection Automation Big Data Cloud Computing Cloud Technology Dask (Software) Deep Learning Derivatives Ensemble Methods Financial Data Financial Instrument Financial Services Forecasting Google Cloud Platform (GCP) Investments Machine Learning Machine Learning Algorithms Management Market Data Microsoft Azure Pattern Recognition Python (Programming Language) PyTorch (Machine Learning Library) Reinforcement Learning Risk Analytics Risk Management Scikit-learn (Machine Learning Library) Stakeholder Management TensorFlow Time Series Xgboost

Summary: This temporary role seeks an ML Quant Engineer with expertise in the financial services sector, specifically within an Investment Bank in London. The position focuses on developing and implementing machine learning models to enhance financial decision-making, particularly in derivatives pricing and risk analytics. The role requires collaboration with quantitative analysts and involves deploying ML solutions for real-time applications. The successful candidate will work on innovative projects that leverage advanced machine learning techniques in a dynamic environment.

Key Responsibilities:

  • Design and implement machine learning models for financial applications, focusing on derivatives pricing, risk analytics, and market forecasting.
  • Build scalable ML pipelines to efficiently process large volumes of financial data.
  • 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.

Key Skills:

  • Advanced Machine Learning Expertise with a deep understanding of ML algorithms and hands-on experience with deep learning architectures.
  • Strong Financial Domain Knowledge, including understanding of financial instruments, derivatives, and risk management principles.
  • Technical Proficiency in Python and familiarity with ML frameworks such as PyTorch, TensorFlow, and JAX.
  • Data Engineering & Infrastructure Skills, comfortable with big data technologies and cloud platforms.
  • Model Optimisation & Deployment Experience, with a proven track record of deploying ML models at scale.
  • Collaborative & Business-Focused, effectively translating financial requirements into ML solutions.
  • Innovative & Analytical Mindset, capable of developing data-driven approaches that complement traditional quantitative models.

Salary (Rate): £1,200.00/daily

City: City Of London

Country: United Kingdom

Working Arrangements: on-site

IR35 Status: inside IR35

Seniority Level: undetermined

Industry: IT

Detailed Description From Employer:

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 £1200 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)
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