Machine Learning Researcher
Optionscitadel Securities
Charing Cross, United Kingdom
1 month ago
Role details
Contract type
Permanent contract Employment type
Full-time (> 32 hours) Working hours
Regular working hours Languages
EnglishJob location
Charing Cross, United Kingdom
Tech stack
Artificial Intelligence
Python
Machine Learning
NumPy
PyTorch
Large Language Models
Deep Learning
Model Validation
Information Technology
Job description
- Own the full research lifecycle, from hypothesis, experiment design, model validation, risk/overfit controls, to deployment
- Conduct cutting-edge research and development in machine learning (e.g. LLMs) at scale with a focus on industry leading techniques and their applications in quantitative finance
- Ship models to production that move P&L in options markets-measured by clear, testable outcomes
- Prototype ? test ? iterate fast The resources and support to take great ideas from concept to trading in a very short space of time
- Discover alpha in high-dimensional data with deep learning, time-series, and representation learning
- Engineer scalable research pipelines from feature generation to distributed training and backtesting
- Develop trading intuition to translate insights into executable strategies
- Leverage large scale compute and data (petabytes; large budgets) to run ambitious experiments and push the frontier
Requirements
- Masters or PhD degree in mathematics, statistics, physics, computer science, or another highly quantitative field
- Advanced training and strong research track record in statistics, machine learning, AI, or another highly quantitative field
- Deep knowledge of cutting edge large scale models and their training and design
- Training techniques (pre-training, fine-tuning, RL, RLHF), and optimization methods
- A results-oriented track record of having taken ML ideas from theory to measurable impact
- Strong math fundamentals (linear algebra, probability, optimization) and mastery of regression/ML for large scale data
- Fluency in Python (NumPy, PyTorch) and the ability to write clean, modular, performant code for large-scale experiments
- Hands-on with modern machine learning (sequence models/transformers, representation learning, regularization, cross-validation, causal/robust inference) applied in practice
- Bias to action & problem-solving demonstrated ability and comfort around owning decisions, iterating quickly, and simplifying complex problems to impactful solutions
- Curiosity about markets and enthusiasm to learn microstructure, options dynamics, and volatility regimes on the job
