Be part of a dynamic team where your distinctive skills will contribute to a winning culture and team.
As a Data Scientist Associate Senior at JPMorgan Chase within the Asset & Wealth Management, youyou 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.
Job responsibilities
- Designs, deployment, and management of prompt-based models leveraging Large Language Models (LLMs) for diverse NLP tasks in financial services.
- Drives research and application of prompt engineering techniques to enhance model performance, utilizing LLM orchestration and agentic AI libraries.
- Collaborates with cross-functional teams to gather requirements and develop solutions that address organizational business needs.
- Communicates complex technical concepts and results effectively to both technical and non-technical stakeholders.
- Builds and maintain robust data pipelines and processing workflows for prompt engineering on LLMs, leveraging cloud services for scalability and efficiency.
- Develops and maintain tools and frameworks for prompt-based model training, evaluation, and optimization.
- Analyzes and interpret data to assess model performance and identify opportunities for improvement.
Required qualifications, capabilities, and skills
- Formal training or certification onData scienceconcepts and 3+ years applied experience
- 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.
- 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.
