Senior Risk Analyst – Data Science & Analytics

Experian · Banking & Financial Services

Mumbai, India
On-site
Posted yesterday
Data & AI
Full-time

About the job

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.

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.

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

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