Multi-Domain Technical Strategy: Lead the development of ML solutions across diverse contexts, ensuring that models for Credit Risk, Pricing Elasticity, and Collection Optimization utilize shared infrastructure efficiently.
MLOps Architecture: Champion the adoption of modern Model Serving frameworks and Feature Stores. Design workflows that ensure feature consistency between training and real-time inference.
Engineering Standards: Establish rigorous standards for Data Versioning, experiment tracking, and Hyperparameter Optimization, ensuring all research is reproducible and production-ready.
Production Deployment: Oversee the transition of models from notebook environments to low-latency production APIs. Ensure models are wrapped, containerized, and integrated seamlessly with backend services.
Mentorship: Guide the team in best practices for Python software engineering, including testing strategies, code structure, and performance optimization.

Experience: 4+ years in Data Science with a strong emphasis on production engineering. Experience in Fintech, Lending, or Risk is highly preferred.
ML Proficiency: Deep understanding of both classical machine learning (Gradient Boosting, Statistical Models) and Deep Learning frameworks.
Production Engineering: Proven track record of deploying models in real-time environments. Familiarity with the concepts of Feature Stores and Model Registries is essential.
Technical Stack: Expert-level Python skills. Strong proficiency in SQL and relational database design.
Strategic Thinking: Ability to translate complex business KPIs (e.g., reducing Non-Performing Loans) into technical ML roadmaps.

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