Join the Data Science team at JP Morgan Asset Management to utilize advanced machine learning and enhance investment processes. We're looking for passionate data scientists with a strong background in Deep Learning and analytical thinking. In this highly collaborative environment, you'll generate actionable insights from a vast collection of data, directly improving investment processes and client experiences. This role offers opportunities for professional growth and learning the latest data science techniques, making a real-world impact in the asset management industry.

Job summary:

As an Asset Management - Data Science Lead - Vice President within JP Morgan Asset Management's Data Science team, you will be focused on enhancing and facilitating various steps in the investment process. This involves utilizing a large collection of textual data including financial documents, analyst reports, news, meeting notes and client communications along with more typical structured datasets. You will have the opportunity to apply the latest methodologies to generate actionable insights to be directly consumed by our business partners. You will also be expected to excel in a highly collaborative environment, working closely with the business, technologists, and control partners to deploy solutions into production.

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 hyper parameters, 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. 3+ years of 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 fine tuning, 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