As a Data Scientist Lead at JPMorgan Chase within the Asset and Wealth Management, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm s business objectives.
Job responsibilities
- Design, deploy and manage prompt-based models on LLMs for various NLP tasks in the financial services domain
- Conduct research on prompt engineering techniques to improve the performance of prompt-based models within the financial services field, exploring and utilizing LLM orchestration and agentic AI libraries.
- Collaborate with cross-functional teams to identify requirements and develop solutions to meet business needs within the organization
- Communicate effectively with both technical and non-technical stakeholders
- Build and maintain data pipelines and data processing workflows for prompt engineering on LLMs utilizing cloud services for scalability and efficiency.
- Develop and maintain tools and framework for prompt-based model training, evaluation and optimization
- Analyze and interpret data to evaluate model performance to identify areas of improvement
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 5+ years applied experience
- Experience with prompt design and implementation or chatbot application
- Strong programming skills in Python with experience in PyTorch or TensorFlow
- Experience building data pipelines for both structured and unstructured data processing.
- Experience 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 and the ability to communicate ideas and results to stakeholders and leadership in a clear and concise manner
- Basic knowledge of deployment processes, including experience with GIT and version control systems
- Familiarity with LLM orchestration and agentic AI libraries
- Hands on experience with MLOps tools and practices, ensuring seamless integration of machine learning models into production environment
Preferred qualifications, capabilities, and skills
- Familiarity with model fine-tuning techniques such as DPO and RLHF.
- Knowledge of Java, Spark
- Knowledge of financial products and services including trading, investment and risk management
