Own data science initiatives across forecasting, classification, experimentation, recommendation, ranking, segmentation, and pricing. Develop and deploy predictive and econometric models for B2B buyer behavior and wholesale market dynamics. Design A/B tests, apply causal inference and regression techniques, collaborate with engineering on MLOps workflows, and communicate analytical recommendations and model limitations to Product, Finance, and Commercial stakeholders.

Own end-to-end data science and machine learning systems, including predictive modeling, forecasting, ranking, and LLM-powered features. Design RAG architectures, evaluate and deploy language models, and maintain reliable production pipelines through MLOps practices. Collaborate with Product, Engineering, and Commercial stakeholders to translate business challenges into measurable ML solutions, while communicating model limitations and trade-offs clearly.