About the role
Define and drive the modeling strategy for the advertising recommendations platform, spanning classical machine learning (for analytics on structured data) and the use of generative AI. You will set the direction on which problems to tackle with ML (e.g. predictive modeling, anomaly detection, clustering) and how to leverage LLMs to maximize user understanding and value. Architect end-to-end machine learning pipelines - oversee the design of data processing workflows, feature stores, model training/validation routines, and deployment mechanisms that can reliably produce daily insights for all customers. Ensure these pipelines are scalable, efficient, and maintainable, working closely with data engineering leaders on implementation. Lead the incorporation of LLM-based components for the platform's intelligent narrative generation. This includes guiding the development of prompt frameworks, fine-tuning strategies, and retrieval-augmented techniques so that the system can answer complex sales questions and explain insights in conversational language. Oversee cross-team initiatives and collaboration, coordinating with engineering, program management, and stakeholder teams. You will chair technical design reviews, balance priorities, and guarantee that the data science efforts align with product
requirements and timelines. Mentor and develop the applied science team, providing technical guidance to other scientists and engineers. Champion best practices in experimentation, coding, and MLOps, and foster a culture of scientific excellence and continuous learning. Ensure robust evaluation and governance of all AI/ML solutions. You will establish metrics for success (accuracy, precision of alerts, coverage of insights), closely monitor model performance in production, and implement processes for periodic retraining, validation, and Responsible AI compliance (addressing bias, fairness, and transparency). Stay ahead of the curve by tracking emerging trends in AI, whether it's new algorithms in anomaly detection or breakthroughs in large language models, and assess their potential to enhance the platform. Drive the incubation of innovative ideas, experimentally verify their
