Perfil de habilidad

scikit-learn

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		Uses scikit-learn for the full ML cycle: preprocessing, model training, evaluation. Applies basic models: LogisticRegression, RandomForest, SVM, KMeans. Works with Pipeline, GridSearchCV, train_test_split for correct ML workflow.

Rol	Obligatorio	Descripción
Data Scientist		Independently solves production tasks with scikit-learn using advanced preprocessing and model selection. Applies ColumnTransformer for heterogeneous data, custom transformers. Uses RandomizedSearchCV, cross_val_predict, and calibration tools.

Rol	Obligatorio	Descripción
Data Scientist		Designs scalable ML solutions with scikit-learn for production. Creates custom estimators, scorers, and cross-validators. Optimizes production pipelines through partial_fit for incremental learning. Integrates scikit-learn with distributed computing.

Rol	Obligatorio	Descripción
Data Scientist		Defines scikit-learn usage standards for the data science team. Establishes shared preprocessing pipelines and model templates. Coordinates decision-making: when scikit-learn is sufficient vs when DL frameworks are needed.

Rol	Obligatorio	Descripción
Data Scientist		Shapes classical ML strategy at organizational level. Defines scikit-learn's role in the ML stack alongside deep learning frameworks. Evaluates emerging alternatives (Polars ML, cuML) and plans migration strategies.

Junior 1 requisitos

Data Scientist

Uses scikit-learn for the full ML cycle: preprocessing, model training, evaluation. Applies basic models: LogisticRegression, RandomForest, SVM, KMeans. Works with Pipeline, GridSearchCV, train_test_split for correct ML workflow.

Middle 1 requisitos

Data Scientist

Independently solves production tasks with scikit-learn using advanced preprocessing and model selection. Applies ColumnTransformer for heterogeneous data, custom transformers. Uses RandomizedSearchCV, cross_val_predict, and calibration tools.

Senior 1 requisitos

Data Scientist

Designs scalable ML solutions with scikit-learn for production. Creates custom estimators, scorers, and cross-validators. Optimizes production pipelines through partial_fit for incremental learning. Integrates scikit-learn with distributed computing.

Lead / Staff 1 requisitos

Data Scientist

Defines scikit-learn usage standards for the data science team. Establishes shared preprocessing pipelines and model templates. Coordinates decision-making: when scikit-learn is sufficient vs when DL frameworks are needed.

Principal 1 requisitos

Data Scientist

Shapes classical ML strategy at organizational level. Defines scikit-learn's role in the ML stack alongside deep learning frameworks. Evaluates emerging alternatives (Polars ML, cuML) and plans migration strategies.

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