Business Analyst - Dallas, TX

Photon Career Site

About The Position

We are looking for a Business Analyst with a deep background in Investment Banking to drive the development of our Agentic AI product suite. You will be the architect of "agent logic," taking intricate financial processes—such as trade reconciliation, regulatory compliance (KYC/AML), or portfolio risk assessment—and decomposing them into tasks that AI agents can perform autonomously. Your goal is to ensure that the AI agents we build are not only technically proficient but also compliant with the rigorous standards of the global financial industry.

Requirements

Domain Expertise: 9+ years as a Business Analyst within an Investment Bank (Front, Middle, or Back Office). Deep understanding of asset classes (Equities, Fixed Income, Derivatives) and financial regulations.
AI/ML Literacy: Proven experience or strong conceptual understanding of LLMs and Agentic workflows (e.g., how agents use tools, memory, and planning to solve tasks).
Data Proficiency: Strong SQL skills and the ability to work with large datasets. Familiarity with Python for data analysis is a significant plus.
Structured Thinking: Ability to turn ambiguous financial problems into "Decision Trees" or "Flow Diagrams" that can be programmed into an agentic framework.
Communication: The ability to explain "Stochastic" AI behavior to traditional bankers and "Capital Markets" complexity to AI engineers.

Nice To Haves

Experience with RAG (Retrieval-Augmented Generation) systems specifically for financial documents (10-Ks, Pitchbooks, Prospectuses).
Certifications such as CFA (Level 1+), CBAP (Certified Business Analysis Professional), or specialized AI product certifications.
Experience in Agile/Scrum environments working alongside AI research teams.

Responsibilities

Process Decomposition & Mapping: Analyze complex IB workflows (e.g., the trade lifecycle, M&A due diligence, or credit risk modeling) and map them into multi-step "agentic" logic.
Requirement Engineering for AI: Write detailed user stories and functional specifications that include "agent instructions," tool-use requirements (e.g., when an agent should call a specific financial API), and reasoning guardrails.
Defining AI "Evals" & Benchmarks: Collaborate with Data Scientists to create "Golden Datasets"—real-world IB scenarios used to test if the AI agent makes the correct financial decisions and follows regulatory protocols.
Compliance & Risk Alignment: Work closely with Legal and Compliance teams to ensure AI agents operate within the bounds of SEC, FINRA, MiFID II, or other relevant frameworks.
Gap Analysis: Identify where current IB data structures (silos, legacy systems) might hinder an AI agent’s ability to retrieve information (RAG) and propose data-cleaning strategies.
UAT & Output Validation: Lead User Acceptance Testing, specifically focusing on the "reasoning" quality of the AI to ensure it doesn't just provide an answer, but follows a logically sound financial path.