Skill-Profil

Feature Engineering

Dieser Skill definiert Erwartungen über Rollen und Level.

Machine Learning & AI Classical Machine Learning

Rollen

wo dieser Skill vorkommt

Stufen

strukturierter Entwicklungspfad

Pflichtanforderungen

die anderen 5 optional

Machine Learning & AI

Classical Machine Learning

22.2.2026

Wählen Sie Ihr aktuelles Level und vergleichen Sie die Erwartungen.

Was wird auf jedem Level erwartet

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

Rolle	Pflicht	Beschreibung
Data Scientist		Creates basic features from structured data: one-hot encoding, label encoding, binning. Applies standard transformations: scaling, normalization, log-transform. Handles missing values through imputation strategies.

Rolle	Pflicht	Beschreibung
Data Scientist		Designs feature engineering pipelines with domain-specific features. Creates temporal features, interaction features, aggregate features. Applies feature selection methods: mutual information, recursive feature elimination, L1 regularization.

Rolle	Pflicht	Beschreibung
Data Scientist		Designs scalable feature engineering systems for production ML. Builds real-time feature computation via feature stores (Feast, Tecton). Applies automated feature engineering (featuretools) and feature drift detection for monitoring.

Rolle	Pflicht	Beschreibung
Data Scientist		Defines feature engineering strategy for the data science team. Establishes shared feature catalog, quality standards, and feature documentation. Coordinates feature platform development and cross-team feature reuse.

Rolle	Pflicht	Beschreibung
Data Scientist		Shapes feature platform strategy at organizational level. Defines centralized feature store architecture for all ML teams. Evaluates AutoML feature engineering and automated feature discovery approaches.

Junior 1 Anforderungen

Data Scientist

Creates basic features from structured data: one-hot encoding, label encoding, binning. Applies standard transformations: scaling, normalization, log-transform. Handles missing values through imputation strategies.

Middle 1 Anforderungen

Data Scientist

Designs feature engineering pipelines with domain-specific features. Creates temporal features, interaction features, aggregate features. Applies feature selection methods: mutual information, recursive feature elimination, L1 regularization.

Senior 1 Anforderungen

Data Scientist

Designs scalable feature engineering systems for production ML. Builds real-time feature computation via feature stores (Feast, Tecton). Applies automated feature engineering (featuretools) and feature drift detection for monitoring.

Lead / Staff 1 Anforderungen

Data Scientist

Defines feature engineering strategy for the data science team. Establishes shared feature catalog, quality standards, and feature documentation. Coordinates feature platform development and cross-team feature reuse.

Principal 1 Anforderungen

Data Scientist

Shapes feature platform strategy at organizational level. Defines centralized feature store architecture for all ML teams. Evaluates AutoML feature engineering and automated feature discovery approaches.

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