AI Scientist — Credit Risk & Agentic AI
StratLytics is hiring an AI Scientist \u2014 Credit Risk & Agentic AI<\/b> for our Bhubaneswar office<\/b> to work on a high\-impact AI underwriting initiative for a North American fintech client.
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This role is ideal for someone who combines credit\-risk analytics, applied machine learning, LLM reasoning, evaluation design, and business judgment. This is not a generic prompt\-engineering role. The selected candidate will help design, test, evaluate, and improve AI agents that analyze credit and underwriting data in a controlled, evidence\-cited, human\-in\-the\-loop environment.
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Key responsibilities include:<\/b>
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\- Design AI reasoning frameworks for credit\-risk and underwriting use cases.
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\- Translate underwriting judgment into structured, testable, and auditable AI workflows.
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\- Work on bureau/tradeline analysis, borrower\-risk signals, shadow debt patterns, data\-integrity flags, and score reconciliation.
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\- Design prompts, reasoning flows, evaluation criteria, and structured outputs for LLM\-based underwriting agents.
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\- Define evaluation frameworks for underwriter alignment, incremental signal, hallucination risk, data\-integrity detection, and decision quality.
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\- Compare AI outputs against historical underwriter decisions, credit scores, model outputs, and repayment/loss outcomes.
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\- Partner with AI engineers, data engineers, credit\-risk experts, and platform teams to build governed AI underwriting solutions.
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\- Participate in internal and client\-facing discussions with senior stakeholders.
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Requirements<\/h3>
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Requirements<\/h3>Required:<\/b>
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<\/div>\- 5+ years of experience in data science, credit\-risk analytics, underwriting analytics, risk modeling, or applied machine learning.
<\/div>\- 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.
<\/div>\- Strong Python and SQL skills.
<\/div>\- Experience with statistical modeling, machine learning, model validation, or analytical backtesting.
<\/div>\- Familiarity with LLMs, prompt design, structured outputs, RAG, or agentic AI systems.
<\/div>\- Ability to define evaluation frameworks and interpret model/AI outputs critically.
<\/div>\- Strong communication skills and ability to work with business, credit, data science, and engineering teams.
<\/div>\- Willingness to work from the StratLytics Bhubaneswar office.
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<\/div>Preferred:<\/b>
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<\/div>\- Experience in fintech, lending, NBFC, banking, credit bureau, SME lending, consumer lending, merchant cash advance, or commercial underwriting.
<\/div>\- Experience with consumer bureau or commercial bureau data.
<\/div>\- Exposure to Claude, OpenAI, AWS Bedrock, LangChain, LangGraph, LlamaIndex, or similar tools.
<\/div>\- Familiarity with Pydantic, JSON schemas, evaluation harnesses, model governance, explainability, or human\-in\-the\-loop AI workflows.
<\/div>\- Prior consulting or client\-facing experience.
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Benefits<\/h3>\- Competitive compensation aligned with market standards, based on experience and capability.
<\/div>\- Opportunity to work on cutting\-edge AI applications in real financial\-services decisioning.
<\/div>\- Exposure to a high\-impact North American fintech AI underwriting programme.
<\/div>\- Work on practical, governed AI systems rather than generic chatbot demos.
<\/div>\- Collaborate with experienced data science, risk, AI, and engineering professionals.
<\/div>\- Opportunity to build expertise in agentic AI, LLM evaluation, credit\-risk analytics, and human\-in\-the\-loop decisioning.
<\/div>\- Candidates from other cities are welcome to apply if they are open to relocating to Bhubaneswar.
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\- 5+ years of experience in data science, credit\-risk analytics, underwriting analytics, risk modeling, or applied machine learning.
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\- 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.
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\- Strong Python and SQL skills.
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\- Experience with statistical modeling, machine learning, model validation, or analytical backtesting.
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\- Familiarity with LLMs, prompt design, structured outputs, RAG, or agentic AI systems.
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\- Ability to define evaluation frameworks and interpret model/AI outputs critically.
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\- Strong communication skills and ability to work with business, credit, data science, and engineering teams.
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\- Willingness to work from the StratLytics Bhubaneswar office.
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Preferred:<\/b>
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\- Experience in fintech, lending, NBFC, banking, credit bureau, SME lending, consumer lending, merchant cash advance, or commercial underwriting.
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\- Experience with consumer bureau or commercial bureau data.
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\- Exposure to Claude, OpenAI, AWS Bedrock, LangChain, LangGraph, LlamaIndex, or similar tools.
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\- Familiarity with Pydantic, JSON schemas, evaluation harnesses, model governance, explainability, or human\-in\-the\-loop AI workflows.
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\- Prior consulting or client\-facing experience.
<\/div><\/span>
Benefits<\/h3>
<\/div><\/span>
Benefits<\/h3>\- Competitive compensation aligned with market standards, based on experience and capability.
<\/div>\- Opportunity to work on cutting\-edge AI applications in real financial\-services decisioning.
<\/div>\- Exposure to a high\-impact North American fintech AI underwriting programme.
<\/div>\- Work on practical, governed AI systems rather than generic chatbot demos.
<\/div>\- Collaborate with experienced data science, risk, AI, and engineering professionals.
<\/div>\- Opportunity to build expertise in agentic AI, LLM evaluation, credit\-risk analytics, and human\-in\-the\-loop decisioning.
<\/div>\- Candidates from other cities are welcome to apply if they are open to relocating to Bhubaneswar.
<\/div><\/span>
<\/div>
\- Opportunity to work on cutting\-edge AI applications in real financial\-services decisioning.
<\/div>
<\/div>
\- Exposure to a high\-impact North American fintech AI underwriting programme.
<\/div>
<\/div>
\- Work on practical, governed AI systems rather than generic chatbot demos.
<\/div>
<\/div>
\- Collaborate with experienced data science, risk, AI, and engineering professionals.
<\/div>
<\/div>
\- Opportunity to build expertise in agentic AI, LLM evaluation, credit\-risk analytics, and human\-in\-the\-loop decisioning.
<\/div>
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\- Candidates from other cities are welcome to apply if they are open to relocating to Bhubaneswar.
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