AI Scientist — Credit Risk & Agentic AI
On-siteBhubaneswar, Odisha, India
Job Summary
Design AI reasoning frameworks for credit-risk and underwriting use cases, translating business judgment into structured, testable workflows. Develop prompts, evaluation criteria, and structured outputs for LLM-based agents that analyze bureau data, tradelines, and shadow debt patterns. Compare AI outputs against historical underwriter decisions and model outputs to define evaluation frameworks for decision quality. Partner with AI engineers, data engineers, and credit-risk experts to build governed underwriting solutions in a human-in-the-loop environment. Participate in internal and client-facing discussions with senior stakeholders.
Required Qualifications
- 5+ years of experience in data science, credit-risk analytics, underwriting analytics, risk modeling, or applied machine learning
- Strong understanding of credit-risk concepts such as bureau data, credit scores, scorecards, delinquency, utilization, inquiries, tradelines, repayment behavior, loss/default outcomes, and underwriting policy
- Strong Python and SQL skills
- Experience with statistical modeling, machine learning, model validation, or analytical backtesting
- Familiarity with LLMs, prompt design, structured outputs, RAG, or agentic AI systems
- Ability to define evaluation frameworks and interpret model/AI outputs critically
- Strong communication skills and ability to work with business, credit, data science, and engineering teams
- Willingness to work from the StratLytics Bhubaneswar office
Desired Qualifications
- Experience in fintech, lending, NBFC, banking, credit bureau, SME lending, consumer lending, merchant cash advance, or commercial underwriting
- Experience with consumer bureau or commercial bureau data
- Exposure to Claude, OpenAI, AWS Bedrock, LangChain, LangGraph, LlamaIndex, or similar tools
- Familiarity with Pydantic, JSON schemas, evaluation harnesses, model governance, explainability, or human-in-the-loop AI workflows
- Prior consulting or client-facing experience
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