About Our Client

Our 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 Applicant

We're looking for someone who has:* 5-9 years of qualified 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 u

📌 Data Science Manager | NBFC (Gurugram)
🏢 Michael Page
📍 Gurugram