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[26/06/2024] Credit Risk Manager
6 days ago
As a member of Risk Models Data Science team, you would be managing the models through their
lifecycle. You will be exposed to activities ranging from (but not limiting to) model development,
evaluation, validation, monitoring, implementation testing and documentation. You will have an
opportunity to learn industry best practices in credit models development and will also be working on experimental models using machine learning techniques. You will also own the models and be a
SPOC for all assessment by Risk Management team.
Responsibilities:
- Credit risk model development using traditional logistic regression and machine learning- techniques- Validation and monitoring of internal and external credit models by computing standard metrics- Detailed documentation of the model development activity for review from Risk Management- Challenging the existing procedures to bring in more efficiency to the model development process- Preparing project presentations for review of Senior Management- Evaluate various business problems across credit function and proposing model-based solutions- Mentoring the junior members in the team for day-to-day activities- Work closely with Cars’24 portfolio teams to understand changes in model performance- Work on multiple projects and perform other duties and/or special projects as assigned.- Provide analytic support on key business initiatives and develop a strong working knowledge of the data warehouse and applicable analytic data systems.- Work independently with stakeholders and propose insights for decision making.
Qualifications/Requirements:
- Masters /Bachelors Degree in Mathematics/Statistics, Operations Research, Economics, Computer Science/Engineering.- 8-10 years relevant analytical experience in Scorecard development, ML modelling, Segmentation and Clustering.- Minimum 2 years- experience in Consumer Credit.- Must have at least 2 years hands on experience in building credit models using SAS or Python or R.- Working knowledge of Logistical/Linear/Regressions CHAID/CART/Clustering techniques.- Experience working in Hadoop and Bigdata environments. Possess strong coding skills in Python and Pyspark.- Ability to decipher complex data and effectively present and communicate in a clear and concise manner.- Strong written/oral communication skills and ability to manage multiple projects simultaneously- 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
Interested candidates can also share their profiles at - along with their Current CTC and Notice Period details.
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