Additionalinformation

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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Companydescription

Company Description

Experian is a global data and technology company, powering opportunities for people and businesses around the world. We operate across a range of markets, from financial services to healthcare, automotive, agribusiness, insurance, and many more. Experian invests in people and new advanced technologies to unlock the power of data. We have an amazing team of 25,200 people in 32 countries.

Jobdescription

Job Description

We are looking for a Risk Analyst – Data Science & Analytics to join our Commercial Bureau Analytics & Pre-Sales Consulting team. In this hands-on role, you'll leverage commercial bureau data, statistical modelling, and machine learning to solve credit risk challenges for banks, NBFCs, fintechs, and MSME lenders. You'll develop bureau-based risk models, scorecards, segmentation and portfolio analytics solutions, while supporting client-focused analytics and pre-sales initiatives. Strong Python, SQL, credit risk modelling, and MSME/commercial lending expertise are key to success in this role.

What you'll do

• Analyze MSME commercial bureau and portfolio data to generate actionable insights across acquisition, underwriting, risk segmentation, portfolio monitoring, early warning, and collections strategies.
• Translate lending and business challenges into structured analytical solutions, defining data requirements, success metrics, risk segments, and modelling approaches aligned to client needs.
• Develop, validate, and optimize bureau-based credit risk scorecards and predictive models using statistical and machine learning techniques, ensuring strong performance, stability, and business impact.
• Engineer robust features from bureau and tradeline data, conduct data quality assessments, perform portfolio analytics, and build scalable analytical datasets using Python and SQL.
• Collaborate closely with clients, pre-sales, Product, and Technology teams to deliver proofs of concept, demonstrate the value of analytics solutions, support technical discussions, and drive successful productisation.
• Create reusable analytics frameworks, features, and modelling assets while maintaining high standards of validation, governance, documentation, compliance, and data security.

Qualifications

What you'll need to bring

• 3+ years of experience in Data Science, Credit Risk Analytics, Decision Science, or Statistical Modelling, with hands-on exposure to lending analytics. Experience in MSME/SME lending and commercial bureau analytics is essential.
• Strong proficiency in Python and SQL, with experience building scalable analytical solutions, performing feature engineering, and working with large, complex credit-risk datasets.
• Hands-on experience developing and validating credit risk scorecards, predictive models, and segmentation frameworks using statistical and machine learning techniques, with a practical understanding of model interpretability and business applicability.
• Strong knowledge of credit risk modelling methodologies, including WoE, IV, variable selection, model validation, calibration, class imbalance, portfolio segmentation, delinquency analysis, early-warning indicators, and performance monitoring.
• Deep understanding of commercial bureau data and lending portfolios, with the ability to derive meaningful risk insights, assess model performance using metrics such as KS, Gini/AUC, Lift, PSI, and translate findings into actionable business recommendations.
• Excellent stakeholder management and communication skills, with the ability to collaborate across business, product, technology, and client teams. Experience with tools and practices such as Git, SAS, Spark, Databricks, model governance, productionisation, consulting, pre-sales, or client-facing analytics engagements is highly valued.