Senior Risk Analyst – Data Science & Analytics
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Protocol Intelligence
Data-driven signals on your job's competitivenessTier-1 brand, metro location, mid-level experience and broad skill set increase candidate competition.
Highly domain-specific MSME credit-risk and bureau analytics reduce cross-industry transferability.
Explicit 5–8 years, 3+ years MSME domain experience, and mandatory Python/SQL and modelling skills impose strict filters.
Job Description
Structured overview of role & requirementsAbout This Role
Lead end-to-end MSME bureau analytics projects covering acquisition, underwriting, risk segmentation, portfolio monitoring, early warning, and collections with full ownership from problem definition to validation and delivery.
Design and develop MSME credit-risk scorecards and predictive models using statistical and machine-learning approaches, ensuring interpretability, stability, and business applicability.
Lead client and pre-sales engagements to diagnose problems, scope proofs of concept, present methodologies, and mentor junior analysts while ensuring compliance with data security and model governance.
Minimum Requirements
5-8 years relevant experience in credit-risk analytics, data science, or statistical modelling, including at least 3 years in MSME/SME/commercial credit-risk or bureau analytics.
Advanced hands-on proficiency in Python and strong SQL skills for handling large granular credit datasets.
Demonstrated end-to-end ownership of credit-risk scorecard or model development from sample design to validation and implementation.
Role location: Mumbai; Work Experience Required: Explicitly stated as 5-8 years with MSME focus; No notice period mentioned.
Ideal Candidate Profile
Experienced in MSME credit-risk and bureau analytics with strong judgement to translate statistical results into commercially usable risk solutions for banks, NBFCs, fintechs, or other MSME lenders.
Capable of leading client-facing pre-sales discussions with senior stakeholders, framing solutions and managing technical queries.
Strong leadership through mentoring junior analysts and improving team standards for quality, reproducibility, and governance of credit-risk analytics.