Manager - Credit

Job Purpose
Risk Management is core to Bajaj Finance. Most of the decisions in Risk Management are data driven and analytical. Statistical models are required to look at multi-variate dimensions from a risk perspective including calculating the expected credit loss, scenario analysis, forecasting, Stress Testing etc. Statistical models are built and scorecards are prepared which assesses parameters like PD (probability of default), EAD and LGD which are critical from a regulatory perspective and forms important aspect of regulatory reporting purpose.This role gives an opportunity of going beyond the above and gives deeper insights on the Regulatory norms on Credit Risk. The role allows candidate work on areas such as Stress Testing, Expected Credit Loss, Macro Economic stress, Macro stress models/forecasting etc.Culture Anchor:Work Hard - Consistently puts in effort. plans and tracks daily progress to achieve targetsExecute with Rigor - Takes responsibility for meeting targets with focus and effortOwn It - Maintains honesty and fairness in all interactions in line with organizations policiesAct with IntegrityDuties and Responsibilities
Build Stress Testing Framework and execute the sameDevelop, validate, and execute Stress Testing Tools and Stress Testing EngineBuild, monitor, validate and track PD, LGD, EAD models for Stress Testing as per RBI guidelines Provide analytical solutions through statistical modeling, credit policy and strategy, reporting and data analysis for the BFL businessesSupport any adhoc deep dive data analysis on portfolio metrices Support in Data analysis and segmentations.Ongoing liaising with IT, Credit and BIU teams to ensure all policies, processes, data flow are working efficiently, and all required changes are build and implemented suitablyRequired Qualifications and Experience
Qualifications

B-Tech/MBA Finance / Postgraduate with 1-3 years in quantitative subjects (Statistics/Data Science)Work Experience

1-3 years relevant analytical experience in Model development, ML modelling, Forecasting, Segmentation and Clustering.

Preferred Coding languages: SAS, SQL, R, Python.

Classical statistical techniques: Regression, Logistic regression, Clustering, Dimensionality reduction techniques, Hypothesis testing.

Experience in handling huge data base and the ability to do root cause analysis.

Individual contributor with the capability to deliver projects within timeline

Effective verbal and written communication skills