Senior Analyst/Associate - Credit Risk & Agentic AI

Client : A fast growing data-science company, serving clients across industries and geographies.

Position :  Senior Analyst / Associate – Credit Risk & Agentic AI
Relevant Experience : 2y to 5y
Location : Bengaluru
Work Mode : In Office only
Joining : Immediate to 30 days
CTC Offered: To discuss

Role Overview
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.

Responsibilities
a) Model Development & Enhancement: Lead and support the end-to-end development / validation of credit risk models (PD, LGD, EAD) and stress testing frameworks.
b) 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.
c) Process Automation: Transition manual analytics workflows into LLM powered agentic workflows to increase throughput and accuracy.
d) Technical Leadership: Guide junior interns and analysts on best practices for Python-based modeling and SQL-driven data architecture.
e) Stakeholder Communication: Translate complex quantitative findings into actionable insights for senior risk committees

Requirements

a) Experience: 2–5 years of hands-on experience in Credit Risk Modeling or analytics-heavy risk projects.

b) 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.

c) Academic Background: Advanced degree in a quantitative field (Statistics, Economics, Math, or Engineering) from a premier institution (Tier1/Tier2).

d) Advanced Tech Stack:  Expert-level Python (Pandas, Scikit-learn, and LLM orchestration libraries). Strong SQL skills for managing complex financial datasets.

e) Certifications (Preferred): Completion of FRM or CFA is highly desirable.