This position is part of the AWM Data Science Center of Excellence (COE) and provides an exciting opportunity to drive impactful Machine Learning solutions within the financial services industry.

As a Data Science Lead in the COE at JPMorgan, you will partner with various lines of business and functional teams to deliver innovative software solutions. You will have the opportunity to research, experiment, develop, and productionize high-quality machine learning models, services, and platforms that create significant business value. Additionally, you will design and implement scalable, reliable data processing pipelines and generate actionable insights to optimize business outcomes.

Key Responsibilities Lead the design, deployment, and management of prompt-based models leveraging Large Language Models (LLMs) for diverse NLP tasks in financial services. Drive research and application of prompt engineering techniques to enhance model performance, utilizing LLM orchestration and agentic AI libraries. Collaborate with cross-functional teams to gather requirements and develop solutions that address organizational business needs. Communicate complex technical concepts and results effectively to both technical and non-technical stakeholders. Build and maintain robust data pipelines and processing workflows for prompt engineering on LLMs, leveraging cloud services for scalability and efficiency. Develop and maintain tools and frameworks for prompt-based model training, evaluation, and optimization. Analyze and interpret data to assess model performance and identify opportunities for improvement. Required Qualifications, Capabilities, and Skills Formal training or certification in software engineering concepts, with 5+ years of hands-on experience in applied machine learning or data science roles. Proven experience in prompt design and implementation, or chatbot application development. Strong programming skills in Python, with expertise in PyTorch or TensorFlow. Experience building data pipelines for both structured and unstructured data. Proficiency in developing APIs and integrating NLP or LLM models into software applications. Hands-on experience with cloud platforms (AWS or Azure) for AI/ML deployment and data processing. Excellent problem-solving skills and the ability to communicate ideas and results clearly to stakeholders and leadership. Working knowledge of deployment processes, including experience with GIT and version control systems. Familiarity with LLM orchestration and agentic AI libraries. Practical experience with MLOps tools and practices to ensure seamless integration of machine learning models into production environments. Preferred Qualifications, Capabilities, and Skills Familiarity with model fine-tuning techniques such as DPO (Direct Preference Optimization) and RLHF (Reinforcement Learning from Human Feedback). Knowledge of Java and Spark.