Senior Analyst – Credit Risk & Agentic AI

About the Role :
We are seeking a forward-thinking Quantitative Analyst with 2 to 5 years of experience to bridge the gap between traditional credit risk modeling and the next generation of autonomous AI. This role is designed for a specialist who understands the rigors of traditional decision scorecard building and/or regulatory modeling (IFRS 9, Basel III, or Stress Testing) but is also actively building Agentic AI frameworks to automate and enhance these processes. You will play a pivotal role in moving beyond static models toward "agentic" systems that can autonomously monitor, report, and suggest calibrations for risk frameworks.

Key Responsibilities  

 Model Development & Enhancement: Lead and support the end-to-end development / validation of credit risk models (PD, LGD, EAD) and stress testing frameworks. 

 Agentic AI Integration: Design and deploy AI Agents to handle complex tasks such as automated model monitoring, document extraction for credit underwritings, or real-time risk signal detection. 

 Process Automation: Transition manual analytics workflows into LLM powered agentic workflows to increase throughput and accuracy. 

 Technical Leadership: Guide junior interns and analysts on best practices for Python-based modeling and SQL-driven data architecture. 

 Stakeholder Communication: Translate complex quantitative findings into actionable insights for senior risk committees

What You’ll Bring
 Experience: 2–5 years of hands-on experience in Credit Risk Modeling or analytics-heavy risk projects. 
 AI Expertise: Demonstrated experience with Proof of Concepts (POCs) or live implementations of Agentic AI (e.g., using frameworks like LangGraph, CrewAI, or AutoGen) specifically for banking or finance. 
 Academic Background: Advanced degree in a quantitative field (Statistics, Economics, Math, or Engineering) from a premier institution. 
 Advanced Tech Stack: * Expert-level Python (Pandas, Scikit-learn, and LLM orchestration libraries). o Strong SQL skills for managing complex financial datasets. 
 Certifications (Preferred): Completion of FRM or CFA is highly desirable