Machine Learning Engineer
Posted 1 day ago by mthree
Negotiable
Undetermined
Undetermined
EC4M, Old Bailey, Greater London
Summary: The Machine Learning Engineer will design and implement machine learning models specifically for financial applications, focusing on pricing and risk analytics. The role involves building scalable ML pipelines, developing deep learning architectures, and optimizing model performance while collaborating with quantitative analysts. The engineer will also support the implementation of ML solutions for derivatives pricing and risk management.
Key Responsibilities:
- Design and implement machine learning models for financial applications, focusing on pricing and risk analytics.
- Build scalable ML pipelines for processing large-scale financial data.
- Develop deep learning architectures for time series prediction, anomaly detection, and pattern recognition in market data.
- Optimize model performance through advanced techniques including hyperparameter tuning, ensemble methods, and neural architecture search.
- Collaborate with quants to understand pricing model requirements and identify ML opportunities.
- Develop data-driven approaches to complement traditional quantitative finance models.
- Support implementation of ML solutions for derivatives pricing and risk management.
Key Skills:
- Deep understanding of ML algorithms (supervised/unsupervised learning, reinforcement learning).
- Extensive experience with neural networks, including RNNs, LSTMs, Transformers.
- Expertise in gradient boosting, random forests, and ensemble methods.
- Experience with generative models (GANs, VAEs, Diffusion models).
- Expert-level Python programming.
- Proficiency with ML frameworks (PyTorch, TensorFlow, JAX).
- Experience with scikit-learn, XGBoost, LightGBM.
- Strong software engineering practices and clean code principles.
- Experience with big data technologies (Spark, Dask).
- SQL and NoSQL databases.
- Cloud platforms (AWS, GCP, Azure).
- Track record of successfully deployed ML models at scale.
- Experience with time series analysis and forecasting.
- Experience applying ML in finance, trading, or risk management contexts.
- Knowledge of stochastic processes and their applications.
- General understanding of financial markets and instruments.
- Basic knowledge of derivatives and their risks.
- Awareness of risk management principles.
Salary (Rate): undetermined
City: London
Country: United Kingdom
Working Arrangements: undetermined
IR35 Status: undetermined
Seniority Level: undetermined
Industry: IT
Detailed Description From Employer:
Machine Learning Engineer / ML Engineer
Machine Learning Development
- Design and implement machine learning models for financial applications, with a focus on pricing and risk analytics
- Build scalable ML pipelines for processing large-scale financial data
- Develop deep learning architectures for time series prediction, anomaly detection, and pattern recognition in market data
- Optimize model performance through advanced techniques including hyperparameter tuning, ensemble methods, and neural architecture search
- Collaborate with quants to understand pricing model requirements and identify ML opportunities
- Develop data-driven approaches to complement traditional quantitative finance models
- Support implementation of ML solutions for derivatives pricing and risk management
Core Technical Skills
Machine Learning Expertise:
- Deep understanding of ML algorithms (supervised/unsupervised learning, reinforcement learning)
- Extensive experience with neural networks, including RNNs, LSTMs, Transformers
- Expertise in gradient boosting, random forests, and ensemble methods
- Experience with generative models (GANs, VAEs, Diffusion models)
Programming & Tools:
- Expert-level Python programming
- Proficiency with ML frameworks (PyTorch, TensorFlow, JAX)
- Experience with scikit-learn, XGBoost, LightGBM
- Strong software engineering practices and clean code principles
Data & Computing:
- Experience with big data technologies (Spark, Dask)
- SQL and NoSQL databases
- Cloud platforms (AWS, GCP, Azure)
Experience
- Track record of successfully deployed ML models at scale
- Experience with time series analysis and forecasting
- Experience applying ML in finance, trading, or risk management contexts
- Knowledge of stochastic processes and their applications
Financial Knowledge
- General understanding of financial markets and instruments
- Basic knowledge of derivatives and their risks
- Awareness of risk management principles