Are you excited about using data science and machine learning to make a real impact in the asset management industry? Do you enjoy working with cutting-edge technologies and collaborating with a team of dedicated professionals? If so, the Data Science team at JP Morgan Asset Management could be the perfect fit for you. Here’s why:

Real-World Impact: Your work will directly contribute to improving investment process and enhancing client experiences and operational process, making a tangible difference in our asset management business. Collaborative Environment: Join a team that values collaboration and teamwork. You’ll work closely with business stakeholders and technologists to develop and implement effective solutions. Continuous Learning: We support your professional growth by providing opportunities to learn and experiment with the latest data science and machine learning techniques.

Job summary: 

The Data Science Lead on the Asset Management Data Science team is focused on enhancing and facilitating various steps in the investment process ranging from financial analysis and portfolio management to client services and advisory. You will use a large collection of textual data including financial documents, analyst reports, news, meeting notes and client communications along with more typical structured datasets. You must excel in working in a highly collaborative environment together with the business, technologists, and control partners to deploy solutions into production. You must also have a strong passion for Machine Learning and invest independent time towards learning, researching, and experimenting with new innovations in the field. You must have solid expertise in Deep Learning with hands-on implementation experience and possess strong analytical thinking, a deep desire to learn and be highly motivated.

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, Hugging Face. 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 explain ability and self validation