Skill-Profil

Gradient Boosting

XGBoost, LightGBM, CatBoost: hyperparameter tuning, feature importance

Machine Learning & AI Classical Machine Learning

Rollen

wo dieser Skill vorkommt

Stufen

strukturierter Entwicklungspfad

Pflichtanforderungen

die anderen 0 optional

Machine Learning & AI

Classical Machine Learning

17.3.2026

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Die Tabelle zeigt, wie die Tiefe von Junior bis Principal wächst.

Rolle	Pflicht	Beschreibung
Data Scientist	Pflicht	Understands gradient boosting principles and its differences from random forest. Trains XGBoost and LightGBM models on tabular data with basic hyperparameter tuning. Interprets feature importance to explain model results.
ML Engineer	Pflicht	Trains XGBoost/LightGBM/CatBoost models with default parameters. Understands gradient boosting concept. Uses feature importance for model analysis.

Rolle	Pflicht	Beschreibung
Data Scientist	Pflicht	Independently trains production-ready gradient boosting models with advanced tuning. Works with XGBoost, LightGBM, and CatBoost, selects the optimal framework. Configures early stopping, regularization, and categorical features handling.
ML Engineer	Pflicht	Performs hyperparameter tuning for gradient boosting (learning_rate, max_depth, n_estimators, regularization). Handles categorical features (CatBoost native, target encoding). Configures early stopping and cross-validation. Analyzes SHAP values.

Rolle	Pflicht	Beschreibung
Data Scientist	Pflicht	Designs gradient boosting systems for production: incremental learning, model compression, ONNX export. Optimizes inference speed through tree pruning and quantization. Implements multi-output boosting and custom objective functions for specific business tasks.
ML Engineer	Pflicht	Designs production gradient boosting systems. Optimizes inference speed (model pruning, quantization). Builds ensembles from multiple gradient boosting models. Integrates with feature store and model serving.

Rolle	Pflicht	Beschreibung
Data Scientist	Pflicht	Defines gradient boosting standards for the data science team. Establishes reusable training pipelines for tabular data. Coordinates the choice between gradient boosting and deep learning for different task types and data.
ML Engineer	Pflicht	Defines gradient boosting usage strategy in ML organization. Evaluates gradient boosting vs deep learning for tabular data. Creates AutoML pipeline.

Rolle	Pflicht	Beschreibung
Data Scientist	Pflicht	Shapes gradient boosting usage strategy at ML platform level. Defines architectural decisions for scalable GBM model training and serving. Evaluates new approaches: differentiable trees, neural-boosting hybrids.
ML Engineer	Pflicht	Defines tabular ML strategy for the organization. Researches novel gradient boosting approaches. Publishes results at conferences.

Junior 2 Anforderungen

Data Scientist
Pflicht

Understands gradient boosting principles and its differences from random forest. Trains XGBoost and LightGBM models on tabular data with basic hyperparameter tuning. Interprets feature importance to explain model results.
ML Engineer
Pflicht

Trains XGBoost/LightGBM/CatBoost models with default parameters. Understands gradient boosting concept. Uses feature importance for model analysis.

Middle 2 Anforderungen

Data Scientist
Pflicht

Independently trains production-ready gradient boosting models with advanced tuning. Works with XGBoost, LightGBM, and CatBoost, selects the optimal framework. Configures early stopping, regularization, and categorical features handling.
ML Engineer
Pflicht

Performs hyperparameter tuning for gradient boosting (learning_rate, max_depth, n_estimators, regularization). Handles categorical features (CatBoost native, target encoding). Configures early stopping and cross-validation. Analyzes SHAP values.

Senior 2 Anforderungen

Data Scientist
Pflicht

Designs gradient boosting systems for production: incremental learning, model compression, ONNX export. Optimizes inference speed through tree pruning and quantization. Implements multi-output boosting and custom objective functions for specific business tasks.
ML Engineer
Pflicht

Designs production gradient boosting systems. Optimizes inference speed (model pruning, quantization). Builds ensembles from multiple gradient boosting models. Integrates with feature store and model serving.

Lead / Staff 2 Anforderungen

Data Scientist
Pflicht

Defines gradient boosting standards for the data science team. Establishes reusable training pipelines for tabular data. Coordinates the choice between gradient boosting and deep learning for different task types and data.
ML Engineer
Pflicht

Defines gradient boosting usage strategy in ML organization. Evaluates gradient boosting vs deep learning for tabular data. Creates AutoML pipeline.

Principal 2 Anforderungen

Data Scientist
Pflicht

Shapes gradient boosting usage strategy at ML platform level. Defines architectural decisions for scalable GBM model training and serving. Evaluates new approaches: differentiable trees, neural-boosting hybrids.
ML Engineer
Pflicht

Defines tabular ML strategy for the organization. Researches novel gradient boosting approaches. Publishes results at conferences.

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