Jigon Yoo
Data & AI-automation engineer: messy data to clean CSV/JSON
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Jigon is ready for their next project!
A schema + range + enum data contract with drift detection for tabular and sensor (rosbag) batches. It catches the batch that still parses but whose meaning changed - a firmware unit switch (Celsius to Fahrenheit), a new category, a sensor dropout - exactly what a naive did-it-parse gate ships. On the committed demo batch: the naive check reports 0 issues; the contract reports 36 violations plus drift (joint_temp mean 45 to 112, z=14.9) on the same data. Runs as a CI gate and names the cause, not the symptom; the clean batch passes untouched. Public code: github.com/jigonyoo/data-contract-guard
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Raw lead and form submissions become CRM-ready records: normalized, classified, scored hot/warm/cold, with a staff summary and reply draft - then routed by a verification gate. Trustworthy leads auto-flow to the CRM; uncertain ones go to a human-review queue. An LLM can label a lead; the value is knowing which to auto-route and which a human must see first - unreachable hot leads and ambiguous spam get caught, not dropped. Runs fully offline (deterministic) or with OpenAI. Public code: github.com/jigonyoo/ai-intake-qualifier
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A FinOps pass over an LLM usage log: it reads a month of calls plus a pricing table and reports how much spend is recoverable - with the dollars behind every recommendation. On the synthetic demo month: 38.9% of spend recoverable, and the single biggest win (moving one classify workload to a smaller model) worth $16.28 on its own. The dollars are small because the demo log is small; the percentages and the ranking port directly to a real invoice. Every figure derives from pricing.json - auditable and reproducible, runnable as a spend gate. Public code: github.com/jigonyoo/llm-cost-optimizer
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Messy sales CSV in, decision-ready report out: cleaning with a data-quality log, KPIs with month-over-month analysis, and an automated report with charts. Anomaly detection is baked into the report - outliers, revenue mismatches and MoM spikes are flagged where they happen, so it is not a pretty-but-blind dashboard. Every cleaning step is logged, so you can see exactly what was fixed, dropped, or flagged before any number reached a chart. Public code: github.com/jigonyoo/analytics-pipeline
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