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Yishi Wang
Junior Machine Learning & Data Science professional specializing in LLMs and analytics
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Amazon internship experience building production GenAI analytics for the returns organization: a multi-agent LLM+RAG system that let analysts query multiple heterogeneous data sources in natural language without hand-written SQL. Also built and operationalized four Apache Airflow DAGs for large-scale ETL, emphasizing observability and freshness-aware metadata to keep outputs accurate and up to date.
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Key Strengths
- Built and deployed a multi-agent GenAI analytics platform with LLM + RAG in production (Amazon returns org)
- Designed metadata catalog + freshness-signal layer to route queries to authoritative, up-to-date data sources and avoid stale SQL generation
- Operationalized 4 Apache Airflow DAG pipelines for large-scale datasets; improved decision cycle from ~6 months to ~1 week
- Reliability/observability focus in orchestration (retries, logging, error handling, monitoring/alerts)
- Defines measurable AI metrics (accuracy, success rate, latency, cost) and uses automated tests + simulations
- Detected production data distribution shift via monitoring and addressed drift by updating/retraining evaluation data
- Effective collaboration with non-technical PMs; translated business needs into an AI analytics solution and iterated via demos
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