Revanth reddy

Senior Data Scientist with 9+ years of progressive experience in credit risk analytics, loss mitigation, and predictive modeling across fintech, lending, and financial services. Proven track record of designing rule-based detection systems, investigating fraud/loss patterns, and developing data-driven credit strategies that maximize eligible customers while controlling losses. Expert in translating complex analytical findings into actionable business strategies through compelling presentations and interactive dashboards.  At Molina Healthcare, led end-to-end design of credit risk and lending prediction models leveraging Python (scikit-learn, XGBoost, LightGBM) and GCP-based ML pipelines, enabling data-driven credit scoring and regulatory-aligned decision systems. Skilled in feature engineering, PD calibration, reject inference, and adverse action analysis, ensuring fairness and interpretability in model-driven lending strategies. Experienced in AUC, KS, Gini, PSI, and CSI metrics for performance monitoring and drift detection across financial portfolios.  Previously at Travelport, built robust credit risk and lending analytics frameworks, engineered feature stores from credit bureau and alternative data sources, and implemented scalable pipelines on Google Cloud Composer, BigQuery, and PySpark. Delivered automated risk scorecards and dashboards that improved portfolio visibility, compliance readiness, and policy precision  At Nationwide, specialized in developing predictive risk models for installment loans, BNPL, and tax refund advances, integrating credit bureau, transactional, and behavioral data to strengthen model accuracy and explainability. Deployed containerized ML workflows via AWS EMR, Glue, and Kubernetes, supporting continuous retraining and governance-compliant credit scoring  Earlier, at Hudda InfoTech and IBing Software Solutions, built a strong foundation in data engineering, ETL pipeline design, and data governance frameworks across Azure and Hadoop ecosystems. Designed enterprise-grade data pipelines, metadata management systems, and feature engineering processes supporting analytical models and business reporting  Adept at working across multi-cloud environments (GCP, AWS, Azure), big data technologies (Spark, Kafka, Hadoop), and data governance frameworks (FCRA, ECOA, GDPR). Recognized for combining deep technical acumen with business insight, translating model outputs into actionable lending policies, and driving responsible, transparent AI practices in financial services.  Credit Risk Strategy Development & Loss Mitigation