Somewhere in the chaos,
there’s a better question.

I developed an XGBoost-based model using feature engineering and hyperparameter tuning, with SHAP explainability to help teams understand what drove its predictions.

Compared with the previous system, the model reduced under-prediction error by 25% and overall prediction error, measured by NRMSE, by 10%. It also used 77% fewer input variables, simplifying the prediction system without sacrificing performance.

Model stability extended from 30 to 360 days—a twelvefold improvement—removing the need for frequent model refreshes and their associated costs. These improvements supported the personalization initiative’s $20M in incremental annual revenue.

Python / XGBoost / SHAP / Feature engineering

The challenge was not simply to remove duplicate variables. Some could be reconstructed from shared base variables, but we needed evidence that the new logic preserved the meaning and behaviour of the originals.

Working across teams, I led the standardisation of variable definitions and shared sources of record. We compared reconstructed variables against their source-based originals, then checked that replacing them kept downstream model results within defined tolerances.

With a strict delivery deadline, I walked leadership through the risks of proceeding without these checks and secured additional time for validation. The broader initiative also included migrating model infrastructure to Google Cloud, with source validation and continuity checks while the models remained operational.

SQL / Google Cloud / Data validation / Cross-functional leadership

The workflow brings together serial validation, component returns, and work-order updates that previously required repeated manual coordination. Built with Oracle AI Agent Studio, it supports concurrent processing across serials while retaining an approval gate before operational changes proceed.

I developed a Java 21 and Micronaut service to replace legacy application logic with APIs, preserving the existing operational rules. Parameterized Oracle SQL, OAuth, and OCI Vault-backed configuration supported the underlying data access and secure integrations.

A consolidated completion email gives users a summary of the batch results. The estimated turnaround improvement compares the previous one-at-a-time process with the concurrent batch workflow; it is not a claim that each individual serial processes 90% faster.

Oracle AI Agent Studio / Java / Micronaut / SQL