Back
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Strong brand, popular Data Scientist title, mid-level experience range, and Gurugram metro increase applicant competition.
Role requires specialized credit-risk, IFRS/CECL knowledge and regulatory model governance, limiting cross-industry transferability.
Explicit 3–8 years, mandatory credit-risk modelling experience, and required Python/SQL skills enforce strict filters.
Develop, implement, and enhance quantitative models (PD, LGD, EAD, IFRS 9/ECL, stress testing, etc.) for credit risk and financial services use cases.
Translate business and risk questions into analytical models and measurable outcomes, including data preparation, methodology design, and performance assessment.
Collaborate with partners and stakeholders to support model validation, documentation, and business implementation.
3 to 8 years of experience in credit risk quantitative modeling including IRB, CECL, IFRS9, predictive modeling, or forecasting models.
Bachelor's or master's degree in Statistics, Mathematics, Economics, Finance, Engineering, Computer Science, Data Science, or related quantitative discipline.
Proficiency in Python and SQL; experience with SAS, R, Spark, or cloud analytics is advantageous.
Knowledge of Model Risk Management framework and related regulatory standards (e.g., SR 11-7, E-23).
Experienced in banking, financial services, consulting, or analytics teams with focus on credit risk and portfolio analytics.
Demonstrates strong analytical judgment with capability to challenge model assumptions and focus on business impact.
Operates independently while effectively collaborating with global teams in a fast-paced consulting environment.