Risk Inn
Website:
riskinn.com
Job details:
๐๐ข๐ซ๐ข๐ง๐ ๐๐๐ง๐๐๐ญ๐ | Credit Risk Modeling, Machine Learning & Analytics Roles
๐จโ๐ผ Openings Across: Multiple Credit Risk Modeling and Analytics Roles
๐ Location: Gurugram, India
๐ Job ID: CR-ML-GUR
๐ผ Experience: 3-7 Years of relevant experience
๐ฐ Compensation: โน25-36 LPA
๐ก ๐๐๐จ๐ฎ๐ญ ๐ญ๐ก๐ ๐๐จ๐ฅ๐:
At Risk Inn, we specialize in connecting leading banks, investment institutions, consulting firms, family offices, and financial services clients with high-quality talent across risk management, quantitative finance, financial markets, investment research, and analytics. Through our curated professional communities and practitioner-driven ecosystem, we bring relevant career opportunities to finance, risk, data, and analytics 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 the Risk & Compliance Analytics practice of a leading global data, AI, analytics, and consulting firm in hiring experienced professionals for multiple credit risk modeling opportunities. These roles are suitable for professionals with strong experience in statistical and machine-learning modeling across credit risk, credit strategy, behavioral scoring, fraud analytics, PD, LGD, IFRS 9, and other banking-focused use cases. Strong proficiency in Python, SQL, NLP, XGBoost, model monitoring, explainability, and stakeholder management will be important for these opportunities.
Think youโre the right fit? Keep reading!
๐๐จ๐ฅ๐: ๐๐ซ๐๐๐ข๐ญ ๐๐ข๐ฌ๐ค ๐๐จ๐๐๐ฅ๐ข๐ง๐ , ๐๐๐๐ก๐ข๐ง๐ ๐๐๐๐ซ๐ง๐ข๐ง๐ & ๐๐ง๐๐ฅ๐ฒ๐ญ๐ข๐๐ฌ
๐๐๐ฒ ๐๐๐ฌ๐ฉ๐จ๐ง๐ฌ๐ข๐๐ข๐ฅ๐ข๐ญ๐ข๐๐ฌ
โ
Develop, validate, and maintain statistical and machine-learning models for credit risk assessment across banking and consumer-lending portfolios
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Build end-to-end models for underwriting, acquisition, behavioral scoring, account management, collections, recovery, fraud detection, PD, LGD, and IFRS 9 use cases
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Extract, clean, and analyze large structured and unstructured datasets using Python and SQL to support model development and performance monitoring
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Perform exploratory data analysis, feature engineering, variable selection, missing-value treatment, class-imbalance handling, model selection, and hyperparameter tuning
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Develop and compare models using logistic regression, gradient boosting, XGBoost, random forest, NLP, and other statistical or machine-learning techniques
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Evaluate model performance through discrimination, calibration, back-testing, stability analysis, benchmarking, and relevant classification metrics
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Apply model-explainability and interpretability techniques to identify key risk drivers and support transparent credit decisions
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Monitor model performance, data drift, population stability, and emerging deterioration, and recommend recalibration, redevelopment, or challenger models where required
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Translate credit-risk and business requirements into analytical solutions while collaborating with client stakeholders across Risk, Underwriting, Collections, Product, Finance, Technology, and Model Validation
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Prepare model-development documentation, methodology notes, validation responses, analytical presentations, and decision-ready recommendations in line with governance and client requirements
๐ ๏ธ ๐๐จ๐ซ๐ ๐๐ค๐ข๐ฅ๐ฅ๐ฌ
- Strong hands-on experience in end-to-end credit risk model development within banking, financial services, fintech, analytics, or consulting
- Advanced proficiency in Python and SQL for data extraction, data preparation, statistical analysis, model development, and monitoring
- Strong practical knowledge of machine learning, XGBoost, Natural Language Processing, logistic regression, gradient boosting, random forest, and classification techniques
- Experience with Python libraries such as Pandas, NumPy, scikit-learn, XGBoost, and relevant NLP libraries or frameworks
- Experience developing acquisition, underwriting, behavioral, collections, recovery, fraud, PD, LGD, or IFRS 9 models
- Strong understanding of retail credit products and lending lifecycle stages, including application assessment, account management, delinquency, collections, recovery, and default
- Knowledge of feature engineering, variable selection, hyperparameter tuning, model calibration, back-testing, benchmarking, and champion-challenger analysis
- Understanding of model-performance measures such as ROC-AUC, Gini, KS, precision, recall, F1 score, confusion matrices, calibration measures, and population-stability indicators
- Knowledge of model explainability, feature importance, fairness assessment, model limitations, and governance expectations for machine-learning models
- Ability to work with structured and unstructured banking data and convert complex analytical results into clear credit-risk and business recommendations
- Strong consulting orientation, problem-solving ability, stakeholder management, presentation skills, written communication, and documentation discipline
๐ ๐๐ก๐๐ญ ๐๐จ๐ฎ ๐๐๐ข๐ง:
โ
Continuous learning and professional development through structured upskilling opportunities across analytics, AI, digital technologies, and domain expertise
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Exposure to global teams, leading financial-services clients, and complex business challenges involving data, analytics, machine learning, and emerging technologies
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Strong career-growth opportunities through high-impact client engagements, internal mobility, recognition, and increased ownership
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Employee-focused and flexible work environment with wellbeing support. Remote opportunities may also be available for exceptional candidates, subject to role fit and business requirements
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-ML-GUR
Along with your resume, please also include 2โ3 lines answering each of the screening questions below in the same email:
๐๐๐ซ๐๐๐ง๐ข๐ง๐ ๐๐ฎ๐๐ฌ๐ญ๐ข๐จ๐ง๐ฌ
Job ID: CR-ML-GUR
- Describe your end-to-end credit risk modeling experience, including the portfolios, business use cases, and model-development stages you have handled.
- Which machine-learning and statistical techniques have you used, including Python, XGBoost, and NLP? Please mention the models developed and the performance metrics used.
- Describe your experience across credit-risk use cases such as credit strategy, PD/LGD, IFRS 9, fraud analytics, or early-warning models, including relevant client-facing or stakeholder-management responsibilities.
OR reach out via DM / WhatsApp +91-885-970-2673 with Job ID CR-ML-GUR
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