Risk Analyst – Data Science & Analytics

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About the role

We are looking for a Risk Analyst – Data Science & Analytics to join our Commercial Bureau Analytics & Pre-Sales Consulting team, with a dedicated focus on MSME bureau analytics. This is a hands-on role for an analyst who can use commercial credit bureau data, statistical modelling and machine learning to solve credit-risk problems for banks, NBFCs, fintechs and other MSME lenders. You will work on bureau-based risk models, scorecards, portfolio diagnostics, early-warning and segmentation use cases, while also supporting proofs of concept and analytically grounded pre-sales solutions. Strong Python and SQL skills, sound credit-risk modelling fundamentals and practical exposure to MSME / SME lending or commercial bureau data are core requirements for this role. What you'll do • Analyse MSME commercial bureau and lender portfolio data to support use cases across acquisition, underwriting, risk segmentation, portfolio monitoring, early warning and collections. • Work with business-entity and facility / tradeline-level bureau information, including repayment and delinquency patterns, credit exposure and outstanding balances, utilisation, enquiries, account vintage, product mix and lender mix; combine these with permitted client, firmographic or financial attributes where relevant. • Translate a lender use case into a structured analytical design, including outcome / bad definition, observation and performance windows, sample construction, segment definitions, data requirements and success metrics. • Develop and validate bureau-based credit-risk scorecards and predictive models for default / serious delinquency risk, risk segmentation and related MSME credit decisions using statistically appropriate techniques. • Engineer robust bureau variables from longitudinal and tradeline data, perform data-quality diagnostics, and create reproducible analytical datasets using Python and SQL. • Evaluate model performance and stability using measures such as KS, Gini / AUC, lift and gains, calibration, out-of-time validation and PSI / CSI, selecting metrics appropriate to the use case. • Perform portfolio analytics such as vintage, cohort, roll-rate, delinquency migration, concentration and risk-segment analysis to identify emerging portfolio trends and actionable insights. • Build rapid but defensible proofs of concept for client opportunities and quantify the incremental value of bureau data, derived variables or analytical approaches over existing baselines. • Support pre-sales consultants in client discovery, analytical solution design, methodology discussions, presentations and responses to technical questions. • Contribute reusable bureau features, modelling utilities, templates and analytical frameworks that improve speed and consistency across recurring MSME use cases. • Work with Product and Technology teams on UAT and productisation of repeatable analytics, and follow applicable data-security, model-governance, documentation and compliance standards. What success looks like • MSME bureau analyses and models are technically sound, reproducible and directly relevant to lending or portfolio decisions. • Client proofs of concept clearly demonstrate analytical value, limitations and expected business impact within agreed timelines. • Reusable bureau variables, code and analytical templates reduce turnaround time and improve consistency across opportunities. • Model development and analytical outputs meet expected standards for validation, documentation, governance and quality. What you'll need to bring • Approximately 3+ years of experience in data science, credit-risk analytics, decision science or statistical modelling, including at least 2 years of meaningful exposure to credit-risk / lending analytics. Direct experience w

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