<div>This role sits at the intersection of analytics, engineering, and business decisioning. You’ll help modernize how we build, deploy, and monitor credit models — ensuring our models not only perform well analytically but are also scalable, explainable, and production-ready.</div><h3>Duties &amp; Responsibilities - Strengthen Credit Risk Modeling and Credit Predictability</h3><ul><li>Build and enhance credit risk models that improve credit predictability and portfolio performance.</li><li>Conduct exploratory data analysis and experiment tracking to identify key risk drivers and optimize model outcomes.</li><li>Partner closely with Credit Strategy, Implementation, and Feature Platform teams to connect model insights directly to decision flows.</li><li>Keep our modeling framework adaptive to changing credit dynamics and economic conditions.</li></ul><h3>Duties &amp; Responsibilities - Bridge Data Science and MLOps for Reliable Model Delivery</h3><ul><li>Own Python project structure, CI/CD setup, and end-to-end testing for reliable model delivery.</li><li>Collaborate with MLOps and Feature Platform teams to maintain model pipelines (e.g., Metaflow, SageMaker, or equivalent orchestration frameworks).</li><li>Support model deployment, validation, and performance consistency in production.</li><li>Drive standardization, automation, and reproducibility across the modeling lifecycle.</li></ul><h3>Duties &amp; Responsibilities - Support Model Monitoring and Governance</h3><ul><li>Expand and maintain monitoring frameworks for production models.</li><li>Support compliance reviews, validation exercises, and performance monitoring (e.g., PSI/CSI analysis, reject inference).</li><li>Maintain audit-ready documentation and ensure transparency across model development and production processes.</li></ul><h3>Requirements</h3><ul><li>Bachelor’s degree in a quantitative field (Statistics, Mathematics, Computer Science, Engineering, Data Science, or related).</li><li>Master’s or PhD preferred.</li><li>Strong understanding of the <b>model lifecycle</b> (EDA, modeling, evaluation, deployment).</li><li>Proficiency in <b>Python</b> (pandas, scikit-learn, etc.) and <b>SQL</b>.</li><li>Experience with <b>Metaflow</b> or similar orchestration frameworks (e.g., Flyte, ZenML, SageMaker Pipelines).</li><li>Familiarity with <b>cloud environments</b> (AWS preferred).</li><li>Experience with <b>model deployment</b> (FastAPI + Docker or similar).</li><li>Understanding of <b>version control</b> (Git/GitHub), <b>unit testing</b> (pytest), and modern Python tooling (e.g., uv, poetry).</li><li>Proven <b>accountability, communication, and work ethic</b> — able to deliver high-quality work on time with minimal oversight.</li></ul><h3>Nice to Have </h3><ul><li>Background in <b>credit risk modeling</b> or <b>lending-related ML applications</b> (e.g., underwriting, loss prediction, fraud detection).</li><li>Experience integrating ML solutions with decision systems or APIs.</li><li>Familiarity with <b>model monitoring frameworks</b> or <b>model governance processes</b>.</li><li>Comfort working in cross-functional teams and presenting technical results to non-technical stakeholders.</li></ul>

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Data Scientist - Credit

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