• Serve as the technical lead for applied ML initiatives that improve the accuracy, precision, and recall of underwriting models. • Design and implement advanced ML training strategies, including AutoML, ensemble learning, and temporal modeling techniques. • Drive GPU-accelerated experimentation, including CUDA-based training optimization and embedding fine-tuning. • Build robust data preprocessing and feature engineering pipelines that can be used in both experimentation and production. • Influence modeling strategy through close collaboration with Pricing Engineering and the ML Science organization. • Deliver measurable improvements to model-driven business outcomes such as conversion rate, rate accuracy, and loan performance. • Mentor future applied ML engineers and help define the long-term roadmap for ML excellence within Pricing.