Lead the strategy, technical vision, and roadmaps for the data quality team to deliver high-quality datasets for DeepMind and Search. Drive the architecture toward establishing the data flywheel for ML training data and Search Context that can self correct and self heal as the data travels through multiple dynamic and large-scale systems (such as sourcing, acquisition and indexing) to the data store. Collaborate closely with DeepMind and Search researchers, data analysts, and engineers to identify key Value of Data (VoD) signals.
Partner with infrastructure owners in AI Foundry to evolve systems—including crawl, processing, and signal enrichment—to deliver high-quality datasets that power Search features and AI models. Provide technical guidance to critical components of the context quality area, such as large-scale signal developments, design and development of quality metrics.
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