Credit Risk Strategy

Job description

**Job Title: Credit Risk Strategy Experience: 3 to 8years** **About Straive:** Straive is a trusted leader in building and operationalizing enterprise data and AI solutions for top global brands and corporations. Our key differentiators include extensive domain expertise across multiple industry verticals, coupled with cutting-edge data analytics and AI capabilities that have consistently driven measurable ROI for our clients. With a global footprint spanning 30+ countries, Straive serves hundreds of clients, supported by a team of 18,000+ seasoned professionals in data, analytics, AI, and operations. Our offices are strategically located across India, the Philippines, the USA, Nicaragua, Vietnam, the UK, Canada, and Singapore, where our headquarters are based. **Job Role:** Manager / Lead - Fraud Risk Strategy Years of Experience: 3 to 6 Years **Job Summary:** We are looking for an experienced **Credit Risk Strategy professional** with **3+ years** of experience in designing and optimizing underwriting strategies using bureau and third party data. The ideal candidate will have hands-on experience in credit decisioning, portfolio performance analysis, and first-party fraud risk management. **Key Responsibilities:** **Underwriting Strategy & Credit Risk** Design, implement, and optimize credit underwriting strategies across the customer lifecycle (originations, line management, renewals, etc.) using bureau and thirdparty data. Develop and refine credit decision rules, cut-offs, and segmentation strategies to balance growth, risk, and profitability. Analyze performance of existing strategies (vintages, cohorts, segments) and recommend data-driven enhancements. Use SQL and Python to extract, transform, and analyze large datasets from multiple internal, bureau, and thirdparty sources. **Fraud Risk (First-Party Focus)** Identify and analyze first-party fraud patterns and behaviors across the credit lifecycle. Design and implement rules, flags, and strategies to detect and prevent first-party fraud while minimizing impact on genuine customers. Partner with operations, collections, and product teams to refine fraud controls and feedback loops. **Required Qualifications:** Bachelor’s or Master’s degree in Finance, Economics, Statistics, Mathematics, Computer Science, Engineering, or a related quantitative field. 3–6 years of hands-on experience in credit risk / underwriting strategy within banking, NBFC, fintech, or credit risk consulting. Demonstrated experience in **designing and implementing underwriting strategies** using **credit bureau data**(e.g., CIBIL, Experian, Equifax, CRIF) and**thirdparty data** **sources** Strong proficiency in:               **SQL** – advanced querying, joins, aggregations, performance tuning for large datasets.               **Python** – data manipulation (pandas), analysis, and basic modeling; ability to productionize or hand over code to engineering teams. Strong analytical and problem-solving skills with the ability to translate data insights into actionable strategies. Excellent communication skills with the ability to present complex analyses and recommendations to non-technical stakeholders.