About the company:

Accumn - a Yubi company, is India’s most advanced AI-first credit decisioning platform, integrating machine learning (ML) and generative artificial intelligence (GenAI) to empower credit managers with exact, data-driven tools for fair and transparent lending decisions. Formed through the strategic consolidation of Corpository and FinFort, Accumn offers a comprehensive suite of solutions to support the entire credit lifecycle—from lead generation and risk management to post- disbursement monitoring. With over 1 lakh active banking and credit users, Accumn has achieved a 66% reduction in credit processing time and boasts an 85% success rate in predicting defaults. The platform has over 5 lakh entities under credit monitoring. The company has been recognized for “Best Use of AI & ML Models for Credit Default Prediction” by Banking Frontiers and “Best Use of AI in Risk Evaluation” by Dun & Bradstreet. Visit www.hello.accumn.ai

and www.go-yubi.com to learn more.

About the Role:





We are looking for an experienced Analyst to spearhead the Analytics initiatives comprising of cutting-edge credit risk assessment models, Unstructured document reading solutions, Data

Analysis and more for our banking, NBFC, and fintech customers. In this pivotal role, you will build credit underwriting and risk management strategies, champion innovation in AI/ML-powered customer journey mapping, and drive the development of transformative product solutions for our lending partners. You will leverage your expertise to deliver industry-leading, technology-enabled credit underwriting and risk assessment solutions—empowering lenders to make precise, data- driven lending decisions and mitigate financial risks effectively.

Education: CA/CFA/MBA(Finance)

Key Responsibilities:
- Exceeding Customer Expectations: Engage proactively with clients, deeply understanding their business requirements. Ensure all analytical models and scorecards not only meet but exceed customer expect

📌 Senior Manager (Ahmedabad)
🏢 Yubi
📍 Ahmedabad