Save Job Back to Search Job Description Summary Similar JobsLead a dynamic team and work in a fast paced environmentBuild and scale high-impact ML solutions with executive visibilityAbout Our ClientOur client is a fintech NBFC.Job Description* Credit Risk Modelling: Design and develop end-to-end credit risk models, including application scorecards, behavioural model development, and portfolio risk modelling.* Supervised Machine Learning: Apply advanced supervised machine learning techniques alongside traditional statistical frameworks like Logistic Regression, Generalized Linear Models (GLM), and XGBoost.* Unsupervised Learning: Utilize unsupervised learning techniques like PCA (Principal Component Analysis) for dimensionality reduction and K-means customer clustering to identify risk segments, fraud vectors, and behavioural patterns.* Regulatory Compliance: Develop and implement regulatory risk modelling solutions aligned with international standards such as BASEL-2 and IFRS9 frameworks.* Risk Metrics Estimation: Own the development of core risk parameters, including Probability of Default (PD), Loss Given Default (LGD), and Expected Credit Loss (ECL).* Performance Benchmarking: Benchmark and continuously improve model performance using appropriate evaluation metrics and experimentation frameworks.The Successful ApplicantWe're looking for someone who has:* 5-9 years of professional experience in applied data science, machine learning engineering, or risk analytics specifically within the BFSI and lending domain.* Proven track record of developing credit risk scorecards, behavioural models, and regulatory frameworks (BASEL-2 / IFRS9 / ECL / PD / LGD).* Demonstrated experience implementing supervised machine learning techniques and unsupervised learning techniques (e.g., PCA, K-means customer clustering).* Strong understanding of statistical fundamentals and neural network fundamentals.* Generative AI: Experience or familiarity with Agentic AI frameworks and Retrieval-Augmented Generation (RAG) architectures.* Deep Learning: Hands-on experience applying deep learning techniques to financial services or credit risk use cases. Advanced LLM Frameworks: Familiarity with prompt engineering, RLHF, and LLM evaluation frameworks.* Governance: Contributions to open-source ML projects, published research, or active participation in responsible AI and strict model governance practices within regulated industries.What's on OfferOpportunity to lead a data science team in the financial services sector.Work in a private banking environment with challenging projects.Quote job refJN-092026-7102936Job summaryFunctionAnalyticsSub SectorRisk AnalyticsWhat is your area of specialisation?Financial ServicesLocationGurgaonJob TypePermanentJob ReferenceJN-092026-7102936
