Data Scientist Associate Senior

As a Data Scientist Associate Senior at JPMorgan Chase within the Asset & Wealth Management , you serve as a seasoned member of an agile team to design and deliver trusted data collection, storage, access, and analytics solutions in a secure, stable, and scalable way. You are responsible for developing, testing, and maintaining critical data pipelines and architectures across multiple technical areas within various business functions in support of the firm s business objectives.

• Supports product managers, data scientists, ML engineers, and other stakeholders to understand requirements.
• Advises and makes custom configuration changes in one to two tools to generate a product at the business or customer request. Design, develop, and deploy state-of-the-art AI/ML/LLM/GenAI solutions to meet business objectives. Develop and maintain automated pipelines for model deployment, ensuring scalability, reliability, and efficiency.
• Updates and implements monitoring mechanisms to track model performance in real-time and ensure model reliability.
• Frequently c onduct thorough evaluations of generative models (eg, GPT-4), iterate on model architectures, and implement improvements to enhance overall performance in NLP applications.
• Adds to team culture of diversity, equity, inclusion, and respect

• Formal training or certification on Machine learning engineering concepts and 3+ years applied experience
• Experience in applied AI/ML engineering, with a track record of developing and deploying business critical machine learning models in production.
• Proficiency in programming languages like Python for model development, experimentation, and integration with Azure OpenAI API.
• Experience with machine learning frameworks, libraries, and APIs, such as TensorFlow, PyTorch, Scikit-learn, and Langchain/Llamaindex.
• Solid understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
• Experience with cloud computing platforms (eg, AWS, Azure, or Google Cloud Platform), containerization technologies (eg, Docker and Kubernetes), and microservices design, implementation, and performance optimization.
• Solid understanding of fundamentals of statistics, machine learning (eg, classification, regression, time series, deep learning, reinforcement learning), and generative model architectures.
• Ability to identify and address AI/ML/LLM/GenAI challenges, implement optimizations and fine-tune models for optimal performance in NLP applications.
• Experience customizing changes in a tool to generate product

• Familiarity with the financial services industries.
• Expertise in designing and implementing AI/ML pipelines.
• Hands-on knowledge of Chain-of-Thoughts, Tree-of-Thoughts, Graph-of-Thoughts prompting strategies, RAG.
• A portfolio showcasing successful applications of generative models in NLP projects, including examples of utilizing OpenAI APIs.