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 reusable analytical code. Strong SQL skills, including independent extraction, transformation and analysis of large, granular credit datasets. Hands-on experience developing credit-risk scorecards or predictive models using techniques such as logistic regression, decision trees, random forests / gradient boosting and segmentation / clustering, with a clear understanding of when interpretability should take precedence over model complexity. Practical knowledge of scorecard and model-development concepts such as binning, Weight of Evidence (WoE), Information Value (IV), variable selection, multicollinearity, train / validation / test design, class imbalance and model calibration. Working knowledge of MSME credit-risk concepts including delinquency and default definitions, portfolio segmentation, vintage analysis, roll rates, risk migration, early-warning indicators and portfolio monitoring. Experience assessing model discrimination, stability and business performance using metrics such as KS, Gini / AUC, lift / gains, PSI / CSI and out-of-time / back-testing approaches. Understanding of bureau data structures, aggregate facility / tradeline information to the business-entity level, identify data-quality issues and derive meaningful behavioural risk features. Strong analytical communication skills, including the ability to explain methodology, findings, assumptions and limitations to business and client stakeholders. Good to have Hands-on experience with commercial credit bureau data, commercial credit reports, bureau scores or bureau-based MSME risk solutions. Experience working with MSME portfolios at banks, NBFCs, fintech lenders, business lenders or analytics / consulting firms serving these institutions. SAS or another statistical programming environment in addition to Python. Git, peer-review practices, Spark / Databricks or other tools used to work with large-scale analytical datasets. Exposure to model implementation, monitoring, challenger frameworks or productionisation of risk analytics. Awareness of model-governance, credit-information and regulatory expectations relevant to lending in India. Client-facing analytics, proof-of-concept, consulting or pre-sales exposure Additional information Great compensation package and discretionary bonus plan Core benefits include pension, health Insurance and term life Insurance, Sharesave scheme and more! 25 days annual leave with 13 bank holidays and 3 volunteering days. You can also purchase additional annual leave. You will report to Senior Analytics Consultant. Role Location: Mumbai Experian is an equal opportunities employer #LI-Onsite Experian Careers - Creating a better tomorrow together Find out what its like to work for Experian by clicking here