THIS INCLUDES:- Collaborate with R&D partners to understand and define their data requirements from inception to delivery- Design and implement ML Data Ops strategies optimized for each feature (collection and annotation), including the identification and sourcing or creation of necessary tooling, equipment or crowd- Drive enhancements of data operations (increase scalability, diversity and quality, reduce cost and lead time), through innovative workflows that combine human and machine computation (leveraging capabilities of ML and foundation models)- Thoroughly scope projects, estimating timelines, cost, and identifying potential challenges in advance- Coordinate data program across internal data functions (data engineering, data science, QA) and other partners- Establish clear guidelines, and training material