Data scientist focused on NLP, anomaly detection, and deployment-minded Python work spanning semantic retrieval, fine-tuning, reusable corpora, and model evaluation.
Built reusable domain corpora for citation-based chatbot and semantic-search prototypes over destination content, reporting archives, and traveler-facing information.
Analyzed AI search, GEO, and organic-discovery trends to guide LLM-readable content structure, prompt-grounding criteria, and evaluation workflows.
Launched 66 surveys and screeners to over 1 million consumers, using response data to refine audience hypotheses and testing inputs for segmentation.
Automated recurring reporting pipelines so destination content and performance data could be reused for retrieval, evaluation, prototyping, and iteration.
Re-platformed R workflows into a one-click Python app, cutting delivery time by 95% for recurring QA, handoff, and labeling cycles.
Built decision-tree models that expanded serial-number tracking by 10x and flagged anomalies with 98% precision for review prioritization and exception follow-up.
Deployed an autoencoder model that improved data quality for downstream analytics, model inputs, and exception handling workflows.
