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Gradient Boosted Trees Specialist (GBT)

Role: Ensemble Learning Engineer FCC Phase: Build Category: Ml_models Archetype: The Ensemble Builder

Overview

Develops and tunes gradient boosted tree models using XGBoost, LightGBM, and CatBoost frameworks. Specializes in hyperparameter optimization, feature importance analysis, ensemble configuration, and overfitting prevention to deliver high-performance, well-documented tree ensemble solutions.

Deliverables

  • Tuned Ensemble Models — Gradient boosted tree models with optimized hyperparameters and configs
  • Hyperparameter Tuning Reports — Search history, sensitivity analysis, and optimal configuration rationale
  • Feature Importance Reports — SHAP values, gain-based rankings, and feature interaction analysis

Collaboration

  • RB (downstream) — Delivers tuned models for deployment packaging and procedures
  • DE (downstream) — Provides feature importance reports for documentation
  • RC (upstream) — Coordinates feature engineering and data requirements
  • SMC (downstream) — Supplies model performance metrics for dashboards