Perfil de habilidad

Feature Engineering

Esta habilidad define expectativas en roles y niveles.

Machine Learning & AI Classical Machine Learning

Roles

donde aparece esta habilidad

Niveles

ruta de crecimiento estructurada

Requisitos obligatorios

los otros 5 opcionales

Machine Learning & AI

Classical Machine Learning

22/2/2026

Selecciona tu nivel actual y compara las expectativas.

Qué se espera en cada nivel

La tabla muestra cómo crece la profundidad desde Junior hasta Principal.

Rol	Obligatorio	Descripción
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.

Rol	Obligatorio	Descripción
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.

Rol	Obligatorio	Descripción
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.

Rol	Obligatorio	Descripción
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.

Rol	Obligatorio	Descripción
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 requisitos

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 requisitos

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 requisitos

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 requisitos

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 requisitos

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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