Eduvanz Financing - Credit Risk Modelling Role

Risk Management is core to Eduvanz. Most of the decisions in Risk Management are data driven and analytical. In order to get faster decision and assess risk of customers during loan acquisition and account management, statistical models are required to look at multi-variate dimensions. Based on that statistical models are built and scorecards are prepared which assesses the probability of default of a customer and based on that different decisions like approve/decline, line assignment and collections are taken.

PRINCIPAL ACCOUNTABILITIES

- Provide analytical solutions through statistical modeling, credit policy and strategy, reporting and data analysis for the Eduvanz businesses

- Monitor, maintain and improve all scorecards, policies and processes across portfolios and ensure its effectiveness

- Support any adhoc deep dive data analysis on portfolio matrices

- Track and improve key performance indicators, losses and portfolio quality. Provide deep dive analysis on portfolio matrices.

- Build statistical models like Application Score, Behaviour Score, Fraud Scores, etc..

- Building of Machine Learning models across the organization as on when required.

- Build, monitor, validate and track PD, LGD, EAD models for ECL as per INDAS guidelines

- Work closely with business team to understand their need and provide Analytical solution.

- Assess if any early warning signals using data analysis and segmentations and take pro-active policy actions as and when required.

- Support in managing and improving various offer strategies, control offer generation and distribution through data analysis

- Work closely with Product, Sales and Risk teams to support business growth and drive new initiatives

- 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 suitably

SKILLS AND KNOWLEDGE

(Minimum acceptable proficiency for this job which best indicates the education and/or experience requirements of this job and not the incumbent)

Educational Qualifications

Qualifications

MBA/Post Graduate with 1-4 years in quantitative subjects

Work Experience

- 2-5 years relevant analytical experience in Scorecard development, ML modelling, Segmentation and Clustering.

- Preferred Coding languages: SQL, R, Python

- Classical statistical techniques: Regression, Logistic regression, Clustering, Dimensionality reduction techniques,

- Machine Learning algorithms: KNN, NBM, DT, CART, Boosting & Bagging models, SVM, Neural net, Ensemble models etc.

- Experience in handling 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