Data Science Manager | NBFC

AimBig Employment Pty Ltd; Gurugram District; On-site; INR 2,000,000 - 3,600,000; Full time

Benefits offered by this job

Lead a data science team

Private banking environment

Job summary

AimBig Employment Pty Ltd in India seeks a senior data scientist focusing on credit risk modelling within a fintech BFSI context. You will design end-to-end risk models, including scorecards, and apply supervised ML alongside traditional statistics.

You will use unsupervised techniques for segmentation, ensure Basel-2/ IFRS9 compliance, and own PD, LGD and ECL estimation. The role requires experience in risk analytics and a track record of deploying models in regulated environments.

Qualifications

• Design and develop end-to-end credit risk models and scorecards.
• Apply supervised ML techniques alongside statistical methods.
• Use unsupervised learning for clustering and risk segmentation.
• Develop regulatory risk models compliant with Basel-2 and IFRS9.
• Estimate PD, LGD and ECL as core risk parameters.
• Benchmark models with appropriate performance metrics.

Responsibilities

• Lead a dynamic data science team in the BFSI sector.
• Develop and scale ML solutions with executive visibility.
• Collaborate on credit risk modelling across lifecycle.
• Ensure governance and regulatory alignment for risk models.
• Implement PD/LGD/ECL estimation methodologies.
• Drive performance improvements through experiments.

Skills

Credit risk modelling

Supervised learning

Unsupervised learning

Regulatory compliance Basel-2 IFRS9

PD/LGD/ECL modelling

Model benchmarking

Job description

• Lead a dynamic team and work in a fast paced environment
• Build and scale high-impact ML solutions with executive visibility

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

• 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 Offer

• Opportunity to lead a data science team in the financial services sector.
• Work in a private banking environment with challenging projects.