ML Modeller - Selby Jennings

• Design, develop, and productionise machine learning models to support trading, pricing, and risk management across asset classes
• Build and deploy NLP/LLM pipelines to extract signals from unstructured financial data (e.g. news, client flow, research, transcripts)
• Develop scalable, end-to-end ML systems, from data ingestion and feature engineering through to model deployment and monitoring
• Apply advanced techniques in machine learning, statistics, and applied mathematics to solve complex market problems
• Conduct rigorous research, backtesting, and validation to ensure robustness, minimise overfitting, and mitigate data leakage
• Collaborate closely with trading, sales, and engineering teams to deliver commercial, front-office solutions
• Build and maintain reusable analytical libraries and contribute to core research infrastructure
• Monitor model performance and manage model lifecycle, including governance, controls, and risk management

• Master's degree (PhD preferred) in a quantitative STEM discipline (e.g. Computer Science, Mathematics, Physics, Engineering)
• Strong hands-on experience in machine learning, data science, and software engineering in a production environment
• Advanced Python programming skills, with experience building scalable, maintainable codebases
• Experience with modern ML frameworks such as PyTorch, TensorFlow, or equivalent
• Strong understanding of statistical modelling, probabilistic methods, and experimental design
• Experience working with large, complex datasets (structured and unstructured)
• Proven ability to design, implement, and deploy end-to-end ML pipelines

NLP / LLM-Focused Experience (Highly Desirable)

• Experience building or working with Large Language Models, including prompt engineering, evaluation, and fine-tuning
• Familiarity with retrieval-based systems (RAG pipelines, embeddings, semantic search, vector databases)
• Experience applying NLP techniques to real-world datasets, particularly in financial or time-sensitive contexts
• Knowledge of model evaluation techniques for LLMs, including guardrails and hallucination mitigation

Technical & Infrastructure Skills

• Experience with data engineering concepts, including ETL pipelines, distributed systems, and data storage solutions (SQL/NoSQL)
• Familiarity with cloud-based ML platforms (e.g. AWS SageMaker, Bedrock) and scalable infrastructure
• Strong understanding of software development best practices (version control, testing, CI/CD, containerisation)
• Experience optimising models and pipelines for performance, latency, and scalability

Domain & Market Experience (Preferred)

• Exposure to financial markets, trading, or quantitative research (e.g. alpha research, eFX, market microstructure)
• Experience working with time-series data and signal generation techniques
• Understanding of trading workflows, pricing models, or risk management frameworks

Leadership & Stakeholder Engagement

• Ability to contribute to strategic direction and innovation within a growing AI capability
• Experience mentoring junior team members and promoting best practices in ML and engineering
• Strong stakeholder management skills, with the ability to translate technical outputs into business impact
• Comfortable working in a collaborative, cross-functional environment with traders, quants, and engineers

Candidate Profile

• Highly analytical and intellectually curious, with a strong problem-solving mindset
• Comfortable operating in a fast-paced, front-office environment with high expectations for impact
• Demonstrates ownership, accountability, and a strong bias toward execution
• Passionate about applying machine learning and LLM technologies to real-world problems in financial markets
• Adaptable, proactive, and motivated to continuously learn and improve