What are the responsibilities and job description for the Data Scientist Senior Associate position at JPMorgan Chase?
Are you passionate about leveraging artificial intelligence and machine learning solutions to solve real-world problems?
As a Data Science Senior Associate, you'll develop scalable tools that integrate Large Language Models, predictive models and generative AI solutions with the Consumer and Community Bank Anti Money Laundering (AML) and Know Your Customer (KYC) Product.
Job Responsibilities:
- Design, develop and deploy AI solutions (classical and generative AI) within the Control and Oversight Product suite of applications.
- Build and maintain APIs to enable seamless integration of AIML models with business applications.
- Use your programming skills in Python and design integrated solutions using the latest technology.
- Ensure all solutions are well documented and aligned with firm wide standards for AIML solutions.
- Analyze diverse data assets and sources to optimize business decisions, developing insights that lead to actionable recommendations for management.
- Build and iterate on prototype solutions.
- Develop robust, production-ready code for AIML models and analytical applications.
- Partner with subject matter experts and help deliver solutions that optimize the data for AIML solutions.
- Understand and apply knowledge of role-related risks, keeping abreast of current regulatory requirements and acting in the shareholder's best interest. Ensure alignment with Firm wide policies and standards governing AIML solutions.
Required qualifications, capabilities and skills:
- Degree in quantitative discipline (e.g., Computer Science, Mathematics, Operations Research, Data Science).
- 5 years' experience in creating predictive models, and generative AI solutions using LLM prompt engineering, Retrieval Augmented Generation (RAG).
- Strong Proficiency in Python.
- Hands-on experience with LLM APIs, Python libraries like Pandas, NumPy, scikit-learn, and others for data manipulation, modeling and analysis.
- Proficiency with data table operations (SQL, etc.).
- Experience designing, building and maintaining ETL data pipelines using tools such as SQL, Python, and Alteryx.
- Demonstrated knowledge of data architecture that is ideal for generative AI solutions.
- Experience with evaluation metrics for ML and generative AI, and with building monitoring dashboards.