Risk Analyst – Data Science & Analytics

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About This Role

Experian is seeking a Risk Analyst for its Commercial Bureau Analytics & Pre-Sales Consulting team based in Mumbai. This on-site position focuses on solving credit risk challenges for banks, NBFCs, fintech lenders, and MSME institutions through statistical modeling, machine learning, and commercial bureau data. The professional chosen for this role will build bureau-based risk models, scorecards, portfolio analytics, and customer segmentation solutions while aiding pre-sales initiatives and client-focused analytics. This…

Job Description

Responsibilities

• Analyze MSME commercial bureau and portfolio data to extract insights for acquisition, underwriting, risk segmentation, portfolio monitoring, early warning, and collections strategies.
• Turn business and lending problems into structured analytical solutions by defining data requirements, metrics, risk segments, and modeling approaches.
• Build, validate, and optimize predictive models and credit risk scorecards via machine learning and statistical methods.
• Engineer features from tradeline and bureau data, conduct data quality checks, execute portfolio analytics, and construct scalable datasets using SQL and Python.
• Partner with clients, technology teams, product groups, and pre-sales to deliver proofs of concept and support technical discussions.
• Construct reusable modeling assets, features, and analytics frameworks while upholding standards for documentation, compliance, data security, and governance.

Requirements

• At least 3 years of professional background in Data Science, Statistical Modelling, Credit Risk Analytics, or Decision Science, including lending analytics exposure.
• Hands-on experience in MSME or SME lending and commercial bureau analytics.
• Proficiency in Python and SQL for building scalable analytical solutions and managing complex credit-risk datasets.
• Practical experience in developing and validating credit risk scorecards, segmentation frameworks, and predictive models.
• Knowledge of credit risk modeling methodologies including WoE, IV, variable selection, model calibration, portfolio segmentation, and early-warning indicators.
• Familiarity with commercial bureau data, lending portfolios, and performance evaluation metrics such as KS, Gini/AUC, Lift, and PSI.
• Stakeholder management and communication capabilities for cross-functional collaboration.

Qualifications

• Experience with Spark, Databricks, SAS, Git, model governance, productionisation, consulting, pre-sales, or client-facing analytics roles.

Core Skills

Benefits

• Compensation package containing a discretionary bonus plan
• Pension, term life insurance, and health insurance
• Sharesave scheme
• 25 days of annual leave along with 13 bank holidays and 3 volunteering days, with an option to purchase extra annual leave

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