Consumer Behavior Modeler II

Bank of America Bank of America · Banking · Charlotte, NC

This role focuses on leveraging GenAI tools and LLM-based solutions to enhance data analysis and insight generation within a financial context. The individual will develop tools for stakeholders to access insights quickly, incorporating analytical knowledge and working with internal data sources and GenAI models. Responsibilities include building statistical models, analyzing data, and developing/implementing LLM solutions, including research into prompting, fine-tuning, embeddings, evaluation harnesses, and guardrails, with a focus on agent frameworks and productionizing prototypes.

What you'd actually do

  1. Leverage GenAI based tools that take natural-language queries and return insights from large datasets
  2. Develop and implement LLM-based solutions to enhance data analysis and insight generation.
  3. Research and evaluate new models and methods (prompting, fine-tuning, embeddings, evaluation harnesses, and guardrails), and develop lightweight prototypes that can be productionized
  4. Demonstrates an advanced understanding of statistical modeling requirements expressed in a technical language and executes per a specified plan and timeline
  5. Builds basic multivariate statistical models and makes logical inferences of relevant statistical assumptions

Skills

Required

  • Strong Python development skills with production-quality engineering practices.
  • Data and infrastructure experience: SQL, Starburst, Teradata, Hadoop, Linux/Unix, and cloud-native or distributed systems.
  • Experience with LLMs and agent frameworks: LangGraph, LangChain, OpenAI-compatible APIs, MCP, vLLM/Triton, multi-agent orchestration, and advanced prompt engineering.
  • Strong hands-on programming experience in Python and SQL.
  • Knowledge of Unix based environment and shell programming.
  • Excellent analytical and problem-solving skills.

Nice to have

  • 3+ years of directly relevant experience
  • Advanced degree in engineering, computer science, statistics or another heavy quantitative discipline
  • Software engineering experience: FastAPI services, containerization, model deployment, and monitoring.

What the JD emphasized

  • production-quality engineering practices
  • Experience with LLMs and agent frameworks
  • multi-agent orchestration

Other signals

  • Leverage GenAI based tools
  • Develop and implement LLM-based solutions
  • Research and evaluate new models and methods (prompting, fine-tuning, embeddings, evaluation harnesses, and guardrails)
  • Experience with LLMs and agent frameworks