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 youll 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.

Qualifications :

What youll 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 :

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

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Remote Work :

No

Employment Type :

Full-time