Description Are you ready to shape the future of investment management with cutting-edge data science? At J.P. Morgan Asset Management, you’ll have the opportunity to create impactful solutions that support our ESG and Stewardship teams. You’ll collaborate with talented professionals, grow your expertise, and make a real difference in how we serve our clients. Join us to advance your career and work on meaningful projects in a dynamic, inclusive environment. As a Senior Data Scientist in Asset Management’s Data Science team, you will design and implement machine learning solutions that enhance our ESG and Stewardship functions. You will work closely with stakeholders across stewardship, ESG, and engineering to build innovative tools from the ground up. Your technical expertise will drive impactful results, and you’ll play a key role in shaping our data science capabilities. You’ll thrive in a collaborative culture that values hands-on problem solving and continuous learning. Job Responsibilities: Collaborate with internal stakeholders to gather requirements and understand business needs Develop technical solutions using LLMs for search, content extraction, and principles-based reasoning Build comprehensive testing packages to ensure solution efficacy and stakeholder trust Design technical architectures and solutions for scalable implementation Partner with engineering teams to deliver high-quality, scalable outputs Stay current with developments in data science and become a subject matter expert Communicate complex concepts and results to both technical and business audiences Required Qualifications, Capabilities, and Skills: Advanced degree in a quantitative or technical discipline, or significant practical industry experience Experience applying NLP, LLM, and ML techniques to solve 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 Strong understanding of ML algorithms including clustering, decision trees, and gradient descent Knowledge of language models, prompt engineering, model finetuning, and domain adaptation Familiarity with deep learning frameworks and their latest developments Ability to communicate complex concepts and results to technical and business audiences Preferred Qualifications, Capabilities, and Skills: Experience in Asset Management Business domain knowledge in ESG, investment stewardship, proxy voting, corporate filings, or buyside investment Familiarity with model explainability and self-validation techniques CFA or equivalent financial qualification