Christian Figueroa

Data Analyst (FinTech

Cometa

Mar 2024 - Current (2 years 1 month)

• Automated a configuration‑validation report (Python + SQL + PDF + Google Sheets) that cut onboarding mis‑config errors from 23 % to 15 %. • Built an end‑to‑end payment‑registration flow — a Remix.js onboarding UI triggers a Python API that writes to PostgreSQL on demand — cutting average payment‑registration ticket resolution time by 25 %. • Wrote ad‑hoc Python ETL scripts for 31 multi‑tenant school onboardings — cleaned & loaded 50 k+ student/tuition. • Delivered training (3 on‑site workshops and 15 live virtual sessions) for school administrators—while acting as the liaison between Product, Onboarding and Engineering converting feedback into actionable tickets and accelerating platform adoption.

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Machine Learning Intern

S4IOT

Jun 2023 - Mar 2024 (10 months)

• Shipped a consumption‑pattern predictor (Python ◦ scikit‑learn) with 85 .9% precision on data from 3 k smart meters, becoming a product's headline marketing feature. • Designed and launched an internal battery‑life prediction platform: built a Hidden Markov Model (75 % precision) and a web UI for on‑demand testing; engineers and support teams now run diagnostics in minutes.

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Software Engineer Intern

S4IOT

Dec 2022 - Jun 2023 (7 months)

• ¿Cuánto Gas? platform — designed the PostgreSQL schema, REST/TypeScript API, and CI/CD pipeline; partnered with a national LPG supplier and a cross‑functional squad (Product, Design, Marketing) to ship the MVP in < 4 months. • Enhanced the core SensiGas platform: implemented new Angular/Node features and refactored legacy raw‑SQL code to an ORM layer, cutting average API response times by 7 % and removing performance bottlenecks that blocked peak‑hour traffic.

Data Analyst Intern (Logistics)

Ingredion

Aug 2021 - Aug 2022 (1 year 1 month)

• Unified sales‑and‑logistics data into a single interactive dashboard (Excel/Power BI), collapsing 13 manual hand‑off steps to 5 and speeding daily decision‑making loops. • Built prioritization models for specialty products and rush orders, optimizing routing and labor allocation. • Conducted a full warehouse‑receiving study with statistical & time‑series methods, redefining KPIs and driving their accuracy up to 6 %, which informed new staffing and inventory strategies.

Christian Figueroa

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