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.
Job DescriptionWe are looking for a Senior 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 senior hands-on role for an experienced credit-risk data scientist who can independently structure complex MSME lending problems, design bureau-led analytical solutions and translate modelling results into client-ready recommendations. You will lead scorecard and model development, portfolio and early-warning analytics, bureau-based proofs of concept and pre-sales solutioning for banks, NBFCs, fintechs and other MSME lenders. The role requires deep Python / SQL capability, strong credit-risk modelling judgement, substantial experience with MSME / SME or commercial bureau analytics, and the ability to guide other analysts while remaining deeply hands-on with data and code.
What you'll do
- Lead end-to-end MSME bureau analytics engagements across acquisition, underwriting, risk segmentation, portfolio monitoring, early warning and collections, from problem definition through validation and delivery.
- Own the analytical design for complex use cases, including outcome / bad definition, observation and performance windows, sample construction, segmentation, treatment of class imbalance, benchmark / challenger design and validation strategy.
- Design advanced bureau variables from longitudinal business-entity and facility / tradeline histories, including repayment behaviour, delinquency patterns, exposure and utilisation, enquiries, account vintage, product / lender mix and changes in credit behaviour over time.
- Develop, benchmark and validate MSME credit-risk scorecards and predictive models using interpretable statistical approaches and machine-learning challengers, making explicit trade-offs between predictive gain, stability, explainability and ease of implementation.
- Lead portfolio diagnostics including vintage, cohort, roll-rate, risk migration, concentration, delinquency-flow and early-warning analysis, and translate findings into actionable credit-risk recommendations.
- Quantify the incremental predictive and business value of bureau variables, scores and analytical constructs through robust benchmark and proof-of-concept designs.
- Lead client and pre-sales discussions to diagnose the problem, assess data feasibility, frame the analytical solution, scope proofs of concept, present methodology and respond to technical questions.
- Convert recurring MSME lender needs into reusable bureau features, analytical frameworks or product enhancements, and work with Product / Technology teams on UAT, implementation and monitoring requirements.
- Review code, model methodology and analytical outputs from other analysts; mentor junior team members for statistical rigour, coding quality, reproducibility and documentation.
- Ensure all analytical work meets applicable data-security, model-governance, documentation and compliance requirements.
What success looks like
- MSME bureau solutions are methodologically defensible, stable, interpretable and clearly linked to a lender decision or portfolio outcome.
- Proofs of concept and client solutioning demonstrate measurable analytical value and materially strengthen opportunity conversion or product adoption.
- Reusable bureau variables and frameworks improve speed-to-solution while complex work is delivered with clear documentation, governance and implementation considerations.
- Team capability improves through strong technical review, mentoring and standardisation of modelling and coding practices.
What you'll need to bring
- Approximately 5-8 years of relevant experience in credit-risk analytics, data science, decision science or statistical modelling, including at least 3 years of substantial experience in MSME / SME / commercial credit-risk or commercial bureau analytics.
- Advanced hands-on proficiency in Python and strong SQL, with demonstrated ability to build efficient, modular and reusable analytical code for large and granular credit datasets.
- Demonstrated end-to-end ownership of credit-risk scorecard or model development, from target and sample design through feature engineering, modelling, validation and implementation / monitoring considerations.
- Deep practical knowledge of scorecard and risk-model development, including binning, WoE / IV, logistic regression, variable selection, multicollinearity, reject-inference considerations where relevant, calibration, segmentation and score scaling, together with experience evaluating tree-based / gradient-boosting challengers.
- Strong command of validation and monitoring concepts including KS, Gini / AUC, lift / gains, calibration, out-of-time validation, back-testing, PSI / CSI, stability and challenger comparisons.
- Strong MSME credit-risk and bureau-data expertise: delinquency / default definitions, underwriting and segmentation, vintage / cohort and roll-rate analysis, early-warning indicators, and conversion of facility / tradeline histories into robust entity-level risk features.
- Experience delivering analytics for banks, NBFCs, fintechs or other business lenders, with the judgement to distinguish statistical results from commercially usable risk solutions.
- Strong client-facing and pre-sales capability, including discovery, solution framing, proof-of-concept design, methodology presentation and handling technical questions from senior risk / analytics stakeholders.
- Experience reviewing analytical work, coaching less-experienced analysts and improving team standards for code quality, validation, documentation and reproducibility.
Good to have
- Direct experience developing or validating commercial credit bureau scores, bureau-based MSME risk models or other bureau-led decisioning solutions.
- Experience across multiple MSME lending products or lifecycle stages, such as business loans, working-capital facilities, secured / unsecured MSME credit, acquisition, underwriting, monitoring and collections.
- SAS or another statistical programming environment in addition to Python.
- Git, peer-review practices, Spark / Databricks or other tools used for large-scale analytical development.
- Experience taking risk models or analytics from proof of concept into production, including monitoring and challenger frameworks.
- Working knowledge of model-governance, credit-information and regulatory expectations relevant to lending in India.
- Experience shaping analytical propositions, reusable solutions or product enhancements from repeated client use cases.
Our uniqueness is that we celebrate yours. Experian's people first, inclusive and purpose driven culture is multi award-winning; World's Best Workplaces™ 2025 (Fortune Global Top 25), Great Place To Work™ in 26 countries to name a few. Check out Experian Life on social or explore our Careers Site to understand why. Experian is also proud to be an Equal Opportunity and Affirmative Action employer. If you have a disability or special need that requires accommodation, please let us know at the earliest opportunity.
Benefits/Perks:
- 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
Recruitment Fraud Awareness - Experian's recruitment process is conducted only through authorised channels. Recruitment communications will only be sent from an @experian.com email address.
Experian will never ask candidates to make any payment as part of an application, interview, assessment, onboarding, or recruitment process. To apply for roles or verify opportunities, please visit experian.com/careers.
Experian Careers - Creating a better tomorrow together
Find out what its like to work for Experian by clicking here
Skills Required
- Approximately 5–8 years of relevant experience in credit-risk analytics, data science, decision science, or statistical modelling, including at least 3 years in MSME, SME, commercial credit-risk, or commercial bureau analytics.
- Advanced hands-on proficiency in Python.
- Strong SQL proficiency and experience building modular, reusable analytical code for large credit datasets.
- End-to-end ownership of credit-risk scorecard or model development, including target and sample design, feature engineering, modelling, validation, implementation, and monitoring.
- Practical knowledge of scorecard and risk-model development, including binning, WoE/IV, logistic regression, variable selection, multicollinearity, reject inference, calibration, segmentation, and score scaling.
- Experience evaluating tree-based or gradient-boosting challenger models.
- Knowledge of validation and monitoring concepts including KS, Gini/AUC, lift/gains, calibration, out-of-time validation, back-testing, PSI/CSI, stability, and challenger comparisons.
- Strong MSME credit-risk and bureau-data expertise, including delinquency/default definitions, underwriting, segmentation, vintage/cohort analysis, roll-rate analysis, early-warning indicators, and entity-level risk features.
- Experience delivering analytics for banks, NBFCs, fintechs, or other business lenders.
- Strong client-facing and pre-sales capability, including discovery, solution framing, proof-of-concept design, methodology presentation, and handling technical questions.
- Experience reviewing analytical work, coaching analysts, and improving standards for code quality, validation, documentation, and reproducibility.
- Direct experience developing or validating commercial credit bureau scores, bureau-based MSME risk models, or bureau-led decisioning solutions.
- Experience across multiple MSME lending products or lifecycle stages.
- SAS or another statistical programming environment in addition to Python.
- Git and peer-review practices.
- Spark, Databricks, or other large-scale analytical development tools.
- Experience taking risk models or analytics from proof of concept into production, including monitoring and challenger frameworks.
- Working knowledge of model-governance, credit-information, and regulatory expectations relevant to lending in India.
- Experience shaping analytical propositions, reusable solutions, or product enhancements from repeated client use cases.
Experian Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Experian and has not been reviewed or approved by Experian.
-
Healthcare Strength — Medical and dental coverage is described as strong, with expanded mental health resources and telemedicine options. Coverage includes inclusive services such as gender transition and fertility support.
-
Leave & Time Off Breadth — Time-off offerings are generous, including substantial PTO/vacation, paid holidays, and paid volunteer days with options to purchase additional leave. Parental leave is available for birth and non-birth parents alongside flexible working arrangements that support work-life balance.
-
Retirement Support — Retirement programs include a 401(k) with company matching and contributory pension schemes in some regions. These elements complement base pay and bonuses to form a competitive total rewards package.
Experian Insights
What We Do
Experian unlocks the power of data to create opportunities for consumers, businesses and society. During life’s big moments – from buying a home or car, to sending a child to college, to growing a business exponentially by connecting it with new customers – we empower consumers and our clients to manage data with confidence so they can maximize every opportunity. We gather, analyse and process data in ways others can’t. We help individuals take financial control and access financial services, businesses make smarter decision and thrive, lenders lend more responsibly, and organizations prevent identity fraud and crime. For more than 125 years, we’ve helped consumers and clients prosper, and economies and communities flourish – and we’re not done. Our 20,600 people in 43 countries believe the possibilities for you, and our world, are growing. We’re investing in new technologies, talented people and innovation so we can help create a better tomorrow. About Experian: Bringing data to life requires creativity, passion, flexibility and expertise. We want you to share in our success. That's why we offer rewards that recognise great performance. Working in a culture of collaboration, achievement and respect we will give you the support and encouragement you need to develop your skills and talents and progress your career. Everyday our people bring enthusiasm, innovation and inspiration to work and if this sounds like you connect with us at Experian.
