Risk Inn
Website:
riskinn.com
Job details:
๐๐๐ซ๐๐๐ซ ๐๐ฉ๐ฉ๐จ๐ซ๐ญ๐ฎ๐ง๐ข๐ญ๐ข๐๐ฌ | Scorecard Modeling, IFRS 9 / ECL Modeling & Credit Risk ML Analytics Roles (2โ5 Years Experience)
๐จโ๐ผ Openings Across: 3 Role Categories
๐ Location: Bangalore, India / Hybrid & Remote, depending on role and project requirements
๐ Job ID: CR-SIM
๐ฐ Salary Range: โน18-30 LPA, subject to experience, role fit, and interview performance
๐ก๐๐๐จ๐ฎ๐ญ ๐ญ๐ก๐ ๐๐จ๐ฅ๐:
At Risk Inn, we specialize in connecting leading banks, investment institutions, consulting firms, and financial services clients with high-quality talent across risk management, quantitative finance, financial markets, and analytics. Through our curated professional communities and practitioner-driven ecosystem, we bring relevant career opportunities to finance and risk professionals globally. Our goal is to bridge the gap between skilled professionals and roles that meaningfully contribute to both individual career growth and organizational impact.
We are supporting our client, a leading global risk analytics and advisory firm, in hiring professionals across scorecard modeling, IFRS 9 / ECL modeling, machine learning-based credit risk analytics, portfolio modeling, and credit risk data science workflows.
These roles are suitable for candidates with 2โ5 years of experience across scorecard development, IFRS 9 / ECL analytics, credit risk modeling, machine learning, portfolio analytics, and predictive risk modeling. Working knowledge of Excel, Python, SAS, SQL, and statistical modeling techniques will be useful depending on the specific role and project requirements.
Think you're the right fit? Keep reading!
๐๐จ๐ฅ๐ ๐: ๐๐๐จ๐ซ๐๐๐๐ซ๐ ๐๐จ๐๐๐ฅ๐ข๐ง๐
๐๐๐ฒ ๐๐๐ฌ๐ฉ๐จ๐ง๐ฌ๐ข๐๐ข๐ฅ๐ข๐ญ๐ข๐๐ฌ
โ
Develop, validate, and monitor credit risk scorecards across retail, SME, and portfolio-level lending products
โ
Support application scorecard, behavioural scorecard, collection scorecard, and PD scorecard development
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Analyze borrower, bureau, transaction, delinquency, and portfolio data to identify risk drivers
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Perform variable selection, binning, weight-of-evidence analysis, information value testing, and model performance checks
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Build and maintain scorecard models using logistic regression and related statistical techniques
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Prepare model documentation, monitoring reports, validation outputs, and business interpretation notes
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Collaborate with credit risk, analytics, business, policy, collections, and technology teams
๐ ๏ธ ๐๐จ๐ซ๐ ๐๐ค๐ข๐ฅ๐ฅ๐ฌ
- 2โ5 years of experience in credit risk scorecard development, credit risk analytics, banking analytics, or related functions
- Strong understanding of credit scorecards, PD models, delinquency behaviour, bureau data, and portfolio risk metrics
- Hands-on experience with logistic regression, WOE, IV, KS, Gini, ROC-AUC, model calibration, and validation checks
- Ability to analyze borrower-level and portfolio-level credit data and convert findings into decision-ready insights
- Good understanding of credit policy, risk segmentation, approval strategies, and scorecard monitoring
- Working knowledge of Excel, Python, SAS, or SQL
- Strong analytical thinking, documentation skills, and attention to detail
- Ability to work with cross-functional teams across risk, business, analytics, policy, and technology
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๐๐จ๐ฅ๐ ๐: ๐๐
๐๐ ๐ / ๐๐๐ ๐๐จ๐๐๐ฅ๐ข๐ง๐
๐๐๐ฒ ๐๐๐ฌ๐ฉ๐จ๐ง๐ฌ๐ข๐๐ข๐ฅ๐ข๐ญ๐ข๐๐ฌ
โ
Support IFRS 9 Expected Credit Loss modeling across credit portfolios
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Analyze PD, LGD, EAD, staging, default, delinquency, and portfolio movement data
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Assist in developing and maintaining lifetime PD, LGD, EAD, and ECL calculation frameworks
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Support staging assessment, significant increase in credit risk analysis, and macroeconomic scenario application
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Perform data extraction, validation, reconciliation, and analysis for impairment and ECL reporting workflows
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Prepare analytical outputs for IFRS 9 reporting, portfolio monitoring, management review, and governance forums
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Support model monitoring, back-testing, sensitivity analysis, overlays, and documentation of assumptions
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Collaborate with credit risk, finance, model risk, reporting, data, audit, and technology teams
๐ ๏ธ ๐๐จ๐ซ๐ ๐๐ค๐ข๐ฅ๐ฅ๐ฌ
- 2โ5 years of experience in IFRS 9, ECL modeling, credit risk modeling, impairment analytics, or related banking functions
- Strong understanding of PD, LGD, EAD, default definitions, staging, lifetime ECL, and macroeconomic scenarios
- Experience working with credit risk data, portfolio performance data, impairment models, and reporting outputs
- Ability to perform data validation, reconciliations, model checks, variance analysis, and assumption reviews
- Good understanding of IFRS 9 reporting requirements, credit risk metrics, and governance expectations
- Working knowledge of Excel, Python, SAS, or SQL
- Strong documentation mindset with attention to auditability, controls, and process clarity
- Ability to work with stakeholders across risk, finance, reporting, model validation, audit, and technology
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๐๐จ๐ฅ๐ ๐: ๐๐๐๐ก๐ข๐ง๐ ๐๐๐๐ซ๐ง๐ข๐ง๐ ๐ฎ๐ฌ๐ข๐ง๐ ๐๐ฒ๐ญ๐ก๐จ๐ง (๐๐ซ๐๐๐ข๐ญ ๐๐ข๐ฌ๐ค)
๐๐๐ฒ ๐๐๐ฌ๐ฉ๐จ๐ง๐ฌ๐ข๐๐ข๐ฅ๐ข๐ญ๐ข๐๐ฌ
โ
Develop and support machine learning models for credit risk analytics, scoring, segmentation, and portfolio monitoring
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Build predictive models for default risk, delinquency risk, collections prioritization, early warning signals, and customer segmentation
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Perform feature engineering, data preprocessing, model training, testing, validation, and performance comparison
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Support challenger model development using techniques such as logistic regression, decision trees, random forest, XGBoost, and similar models
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Evaluate model performance using metrics such as ROC-AUC, KS, Gini, precision, recall, stability, and population drift
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Prepare model documentation, explainability outputs, monitoring reports, and business interpretation notes
โ
Collaborate with credit risk, analytics, data science, business, model validation, and technology teams
๐ ๏ธ ๐๐จ๐ซ๐ ๐๐ค๐ข๐ฅ๐ฅ๐ฌ
- 2โ5 years of experience in credit risk analytics, machine learning, data science, predictive modeling, or banking analytics
- Hands-on experience using Python for data analysis, feature engineering, model development, and model evaluation
- Working knowledge of machine learning models such as logistic regression, decision trees, random forest and XGBoost
- Ability to validate model outputs, explain drivers, identify data issues, and convert model results into business-ready insights
- Good understanding of model performance metrics, model monitoring, explainability, and governance requirements
๐ ๐๐ก๐๐ญ ๐๐จ๐ฎ ๐๐๐ข๐ง:
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Opportunity to work with a leading global risk analytics and advisory firm
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Exposure to scorecard modeling, IFRS 9 / ECL analytics, credit risk modeling, and machine learning workflows
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Hybrid and remote role opportunities
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Strong learning curve across portfolio analytics, predictive modeling, risk frameworks, and model governance
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Opportunity to work on high-impact credit risk analytics and modeling initiatives
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Exposure to stakeholders across risk, analytics, finance, reporting, audit, and technology teams
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Collaborative environment with strong professional growth opportunities
โฐ Ready to take the next step? Timing is key!
๐ฉ Send your resume to empowering@riskinn.com
Subject Line: Application for Job ID CR-SIM
OR reach out via DM / WhatsApp +91-885-970-2673 with Job ID CR-SIM
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