We 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.
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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
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