Risk Analyst – Data Science & Analytics

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 with MSME / SME / commercial lending or commercial bureau analytics is required. Strong hands-on proficiency in Python for data manipulation, feature engineering, statistical analysis and machine learning, with the ability to write structured and reu