JPMorgan NLP/LLM Data Scientist Lead - Asset Management & Analytics 

United Kingdom, England, London 

355761178

Job Responsibilities

• Collaborate with internal stakeholders to identify business needs and develop NLP/ML solutions that address client needs and drive transformation.
• Apply large language models (LLMs), machine learning (ML) techniques, and statistical analysis to enhance informed decision-making and improve workflow efficiency, which can be utilized across investment functions, client services, and operational process.
• Collect and curate datasets for model training and evaluation.
• Perform experiments using different model architectures and hyperparameters, determine appropriate objective functions and evaluation metrics, and run statistical analysis of results.
• Monitor and improve model performance through feedback and active learning.
• Collaborate with technology teams to deploy and scale the developed models in production.
• Deliver written, visual, and oral presentation of modeling results to business and technical stakeholders.
• Stay up-to-date with the latest research in LLM, ML and data science. Identify and leverage emerging techniques to drive ongoing enhancement.

Required qualifications, capabilities, and skills

• Advanced degree (MS or PhD) in a quantitative or technical discipline or significant practical experience in industry.
• Commercial experience in applying NLP, LLM and ML techniques in solving high-impact business problems, such as semantic search, information extraction, question answering, summarization, personalization, classification or forecasting.
• Advanced python programming skills with experience writing production quality code
• Good understanding of the foundational principles and practical implementations of ML algorithms such as clustering, decision trees, gradient descent etc.
• Hands-on experience with deep learning toolkits such as PyTorch, Transformers, HuggingFace.
• Strong knowledge of language models, prompt engineering, model finetuning, and domain adaptation.
• Familiarity with latest development in deep learning frameworks.
• Ability to communicate complex concepts and results to both technical and business audiences.

Preferred qualifications, capabilities, and skills

• Prior experience in an Asset Management line of business
• Exposure to distributed model training, and deployment
• Familiarity with techniques for model explainability and self-validation