Data Engineering and Integration: Build the data and knowledge pipelines required for AI use cases, with a focus on reliable ingestion, preparation, retrieval, and access-control-aware use of information.
Design experiments and evaluate outcomes. Conduct offline evaluations, backtesting and online A/B experiments to assess model performance and business impact. Investigate data quality, feature effectiveness and evaluation bias, and use findings to guide further development.
Develops a good working knowledge of materials and sources of supply to make purchases at prices consistent with quantity and quality requirements and within established standard costs.