AM - Model Development

Job Purpose

We are looking for Assistant Manager to join our team.

In this role, you will be responsible for developing data‑driven, analytically rigorous solutions that enable informed and timely decision-making across the credit lifecycle. The role involves building machine learning models, improving analytical processes, driving automation, and contributing to advanced risk intelligence initiatives. A strong passion for modelling, experimentation, and problem-solving is essential.

Role

1. Model Development & Advanced Analytics

• Develop and implement credit risk models (PD, LGD, EAD), fraud detection algorithms, collection prioritization models, customer propensity models, and other machine learning‑based solutions.
• Apply statistical techniques and ML algorithms to solve complex business problems across underwriting, portfolio monitoring, and collections.

2. Insight Generation & Risk Monitoring

• Identify data patterns, uncover trends, and generate actionable insights to strengthen credit decision-making.
• Perform deep-dive analysis on emerging customer behavior and macro trends to support early warning signals and risk mitigation strategies.

3. Data Engineering & Process Improvement

• Collaborate with technology teams to enhance data pipelines, improve data quality, and enable scalable model deployment.
• Contribute to the design, testing, and implementation of production-grade analytical systems.

4. Stakeholder Management

• Present analytical findings, recommendations, and model outcomes to business, risk, and leadership teams in a clear and structured manner.

5. Innovation & Alternate Data Exploration

• Explore and evaluate alternate data sources to identify new opportunities for credit risk optimization, segmentation, and underwriting enhancements.

Qualification & experience

Bachelor’s or Master’s degree in Engineering, Economics, Statistics, Mathematics, or any quantitative discipline.2–4 years of relevant experience in risk analytics, preferably in BFSI.

Essential skills

• Strong proficiency in SQL for complex data extraction and analysis.
• Hands-on experience in Python and familiarity with industry-standard ML libraries.
• Prior exposure to statistical modelling, scorecard development, or policy analytics.