Senior Risk Analyst – Data Science & Analytics

Job Description 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.