EWS-Data Scientist

Bahwan Cybertek Group; Mumbai, Chennai District, Bengaluru; On-site; INR 700,000 - 1,100,000; Full time

Job summary

Bahwan Cybertek Group is seeking a skilled ML Risk Analyst to design, build, and validate Early Warning System scorecards for retail and corporate portfolios. The role focuses on end-to-end model development, including feature selection, hyperparameter tuning, and cross-validation, with governance and documentation at the core.

You'll monitor production scorecards, assess OOT validity, and report results to Risk, IT, and Collections teams.

Qualifications

• Strong grounding in ML fundamentals (classification techniques, CV, hyperparameter tuning).
• Proficiency in Python or SAS for model development and data analysis.
• Working knowledge of SQL for data extraction.
• Experience in credit risk analytics, specifically scorecard modelling and model validation.
• Ability to audit a model's implementation against its stated methodology.
• Strong written and verbal communication skills for stakeholder engagement.
• Experience working with bank Risk, Collections, and IT teams in a consulting capacity.

Responsibilities

• Design, build, and document EWS/behavioral scorecards using ML techniques.
• End-to-end model development: sample design, target definition, feature selection, tuning, cross-validation.
• Validate models and review vendor-built or in-house models for governance gaps.
• Monitor live scorecards with OOT validation and track Gini/AUROC; flag anomalies.
• Prepare Model Validation Documents and governance checklists per MRM frameworks.
• Prepare Model Monitoring reports on a quarterly basis.
• Liaise with bank IT teams to translate model logic into implementation-ready specs.
• Present model findings and validation outcomes to Retail Risk, Collections, and stakeholders.

Skills

Machine learning fundamentals

Python

SAS

SQL

Model validation

Documentation

Communication

Banking risk

Consulting

FRM certification

Education

FRM certification

Job description

• Design, build, and document Early Warning System (EWS) / behavioral scorecards (retail and corporate/wholesale) using machine learning techniques such as XGBoost, logistic regression, and random forest.
• Perform end-to-end model development: sample design, target definition (e.g. SMA/NPA flagging), feature selection and hypothesis documentation, hyperparameter tuning (e.g. Optuna), cross-validation (k-fold/OOF), and threshold optimization.
• Conduct independent model validation and technical review of vendor-built or in-house models -- including checking implementation code against validation reports, verifying variable weights/coefficients, and identifying documentation or governance gaps.
• Monitor live/production scorecards through Out-of-Time (OOT) validation, tracking discriminatory power (Gini, AUROC), score stability, and population drift; flag and investigate anomalies (e.g. OOT Gini exceeding development Gini).
• Prepare Model Validation Documents, Variable Hypothesis reports, and governance checklists in line with AI/ML Model Risk Management (MRM) frameworks.
• Prepare Model Monitoring reports on a quarterly basis.
• Liaise with bank IT teams to translate model logic into implementation-ready specifications
• Present model findings, enhancements, and validation outcomes to Retail Risk, Collections, and other stakeholders at development and each reporting cycle.

Required Skills & Experience

• Strong grounding in machine learning fundamentals (classification techniques, cross-validation, hyperparameter tuning, evaluation metrics such as Gini/AUROC/KS).
• Proficiency in Python or SAS for model development and data analysis (Python preferred; pandas, scikit-learn, XGBoost, Optuna or equivalent).
• Hands-on experience in credit risk analytics, specifically scorecard modelling (behavioral/EWS), model validation, and ongoing portfolio monitoring.
• Working knowledge of SQL for data extraction and variable sourcing from banking systems.
• Experience preparing formal model documentation -- validation reports, variable hypothesis documents, governance/AI-ML checklists -- suitable for regulatory/MRM review.
• Ability to independently audit a model's implementation (code) against its stated methodology and flag discrepancies.
• Strong written and verbal communication skills for stakeholder presentations and technical write-ups.
• Experience working directly with bank Risk, Collections, and IT teams in a consulting capacity.

Required Skills

• Design, build, and document Early Warning System (EWS) / behavioral scorecards (retail and corporate/wholesale) using machine learning techniques such as XGBoost, logistic regression, and random forest.
• Perform end-to-end model development: sample design, target definition (e.g. SMA/NPA flagging), feature selection and hypothesis documentation, hyperparameter tuning (e.g. Optuna), cross-validation (k-fold/OOF), and threshold optimization.
• Conduct independent model validation and technical review of vendor-built or in-house models -- including checking implementation code against validation reports, verifying variable weights/coefficients, and identifying documentation or governance gaps.
• Monitor live/production scorecards through Out-of-Time (OOT) validation, tracking discriminatory power (Gini, AUROC), score stability, and population drift; flag and investigate anomalies (e.g. OOT Gini exceeding development Gini).
• Prepare Model Validation Documents, Variable Hypothesis reports, and governance checklists in line with AI/ML Model Risk Management (MRM) frameworks.
• Prepare Model Monitoring reports on a quarterly basis.
• Liaise with bank IT teams to translate model logic into implementation-ready specifications
• Present model findings, enhancements, and validation outcomes to Retail Risk, Collections, and other stakeholders at development and each reporting cycle.
• Experience reviewing or validating models built by external vendors.
• Exposure to logistic regression-based legacy scorecards alongside modern ML approaches.
• FRM certification.