Data Scientist [multiple Positions Available]

JPMorgan Chase JPMorgan Chase · Banking · Plano, TX +1 · Commercial & Investment Bank

This role focuses on building and managing reports, dashboards, and analytical tools, migrating to cloud architectures, and leading the design, prototyping, and strategy for advanced modeling, predictive analytics, and generative AI solutions for Merchant Services. It involves orchestrating AI agent systems, developing BI products using generative AI and LLMs, prompt engineering, fine-tuning, and RAG, as well as designing statistical and dynamic pricing models. The role also includes product pricing strategy, profitability analysis, and modeling network fee impacts.

What you'd actually do

  1. Lead design, prototyping and strategize advanced modeling, predictive analytics and generative AI solutions for Merchant Services, with a focus on data-driven sales.
  2. orchestrating autonomous Artificial Intelligence agent systems and developing business intelligence products using generative AI and Large Language Models(LLMs) in Python, leveraging prompt engineering, fine-tuning LLMs, and implementing Retrieval-Augmented Generation (RAG) systems
  3. Lead product pricing strategy by providing market rate estimates and recommendations that balance client savings with firm profitability.
  4. Model the potential and future impact of network fee changes on product economics, ensuring informed decision-making and long-term financial sustainability.
  5. Build and manage reports, dashboards, and analytical tools using business intelligence platforms to analyze payment data, spearheading migration from on-premises and legacy platforms to modern, cloud-based architectures.

Skills

Required

  • leveraging understanding of finance, banking, and payments to analyze data and build business intelligence products
  • using SQL to write queries, optimize performance, and manage datasets
  • working in cloud based data platforms including Snowflake and Databricks
  • utilizing productivity and collaboration tools including Bitbucket, Jira, and Confluence for development
  • utilizing Agile development, leveraging Agile and Scrum methodologies to deliver projects
  • orchestrating autonomous Artificial Intelligence agent systems and developing business intelligence products using generative AI and Large Language Models(LLMs) in Python, leveraging prompt engineering, fine-tuning LLMs, and implementing Retrieval-Augmented Generation (RAG) systems
  • designing and implementing statistical modeling using SQL and Python to build predictive analytics and Dynamic pricing models
  • collaborating with data scientists, governance teams, data owners, and engineering teams to develop business intelligence tools

What the JD emphasized

  • autonomous Artificial Intelligence agent systems
  • generative AI
  • Large Language Models(LLMs)
  • prompt engineering
  • fine-tuning LLMs
  • Retrieval-Augmented Generation (RAG) systems

Other signals

  • generative AI
  • LLMs
  • RAG
  • prompt engineering
  • fine-tuning LLMs
  • autonomous Artificial Intelligence agent systems