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Strong fintech brand, metro location, mid-level data scientist role, and generalist ML requirements increase competition.
Credit-risk and regulatory model governance require domain-specific experience, limiting cross-industry transferability.
Explicit 5+ years, production ML deployment, credit governance and MLOps requirements make filters highly stringent.
Design, build, and deploy machine learning models for credit scoring, risk profiling, fraud detection, and customer segmentation.
Ensure model governance through documentation, internal and credit reviews, and regulatory compliance.
Lead and mentor junior data scientists; develop model pipelines and monitoring frameworks to drive data-driven insights and operational efficiency.
5+ years of data science experience, preferably in fintech or financial services.
Proven experience in developing and deploying machine learning models in production.
Experience with credit modelling, risk management, and formal model governance processes in regulated environments.
Work Experience Required: 5+ years in relevant fields.
Experienced in end-to-end credit risk modelling and deployment in regulated environments.
Capable of leading data science initiatives and mentoring junior team members.
Skilled at building automated, scalable model infrastructure and translating analytics into actionable business insights.