Perfil de habilidad

ML Model Evaluation

Esta habilidad define expectativas en roles y niveles.

Machine Learning & AI MLOps

Roles

donde aparece esta habilidad

Niveles

ruta de crecimiento estructurada

Requisitos obligatorios

los otros 10 opcionales

Machine Learning & AI

MLOps

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		Understands core ML metrics: accuracy, precision, recall, F1, ROC-AUC for classification; RMSE, MAE, R² for regression. Conducts cross-validation for generalization assessment. Builds confusion matrix and classification report via scikit-learn.
LLM Engineer		Knows basic ML metrics: accuracy, precision, recall, F1. Computes metrics for classification and regression models used in LLM system preprocessing pipelines.

Rol	Obligatorio	Descripción
Data Scientist		Applies advanced evaluation methods: stratified cross-validation, time-series split, nested cross-validation. Evaluates models considering business metrics: lift, gain charts, expected calibration error. Analyzes models for fairness and bias via disaggregated metrics.
LLM Engineer		Independently conducts comprehensive ML model evaluation: confusion matrix, ROC-AUC, calibration plots. Evaluates auxiliary ML models in LLM pipelines: safety classifiers, intent detectors.

Rol	Obligatorio	Descripción
Data Scientist		Designs comprehensive evaluation frameworks for ML models: offline metrics, online metrics, business KPIs. Implements automated model validation gates before production deployment. Applies counterfactual analysis and SHAP for deep model diagnostics.
LLM Engineer		Designs evaluation frameworks for ML components of the LLM ecosystem: cross-validation strategies, statistical significance testing, fairness metrics. Automates regression testing.

Rol	Obligatorio	Descripción
Data Scientist		Defines model evaluation standards for the data science team. Establishes evaluation checklist for each ML task type. Coordinates model review process and alignment between offline metrics and business outcomes.
LLM Engineer		Defines ML evaluation standards for the LLM team. Establishes guidelines for auxiliary ML model assessment, threshold selection, and A/B testing methodology.

Rol	Obligatorio	Descripción
Data Scientist		Shapes ML quality assurance strategy at organizational level. Defines enterprise model validation standards and audit requirements. Publishes evaluation methodologies for industry and shapes thought leadership.
LLM Engineer		Shapes enterprise ML evaluation strategy. Defines approaches to model quality governance, automated evaluation pipelines, and alignment of ML metrics with business KPIs at organizational scale.

Junior 2 requisitos

Data Scientist

Understands core ML metrics: accuracy, precision, recall, F1, ROC-AUC for classification; RMSE, MAE, R² for regression. Conducts cross-validation for generalization assessment. Builds confusion matrix and classification report via scikit-learn.
LLM Engineer

Knows basic ML metrics: accuracy, precision, recall, F1. Computes metrics for classification and regression models used in LLM system preprocessing pipelines.

Middle 2 requisitos

Data Scientist

Applies advanced evaluation methods: stratified cross-validation, time-series split, nested cross-validation. Evaluates models considering business metrics: lift, gain charts, expected calibration error. Analyzes models for fairness and bias via disaggregated metrics.
LLM Engineer

Independently conducts comprehensive ML model evaluation: confusion matrix, ROC-AUC, calibration plots. Evaluates auxiliary ML models in LLM pipelines: safety classifiers, intent detectors.

Senior 2 requisitos

Data Scientist

Designs comprehensive evaluation frameworks for ML models: offline metrics, online metrics, business KPIs. Implements automated model validation gates before production deployment. Applies counterfactual analysis and SHAP for deep model diagnostics.
LLM Engineer

Designs evaluation frameworks for ML components of the LLM ecosystem: cross-validation strategies, statistical significance testing, fairness metrics. Automates regression testing.

Lead / Staff 2 requisitos

Data Scientist

Defines model evaluation standards for the data science team. Establishes evaluation checklist for each ML task type. Coordinates model review process and alignment between offline metrics and business outcomes.
LLM Engineer

Defines ML evaluation standards for the LLM team. Establishes guidelines for auxiliary ML model assessment, threshold selection, and A/B testing methodology.

Principal 2 requisitos

Data Scientist

Shapes ML quality assurance strategy at organizational level. Defines enterprise model validation standards and audit requirements. Publishes evaluation methodologies for industry and shapes thought leadership.
LLM Engineer

Shapes enterprise ML evaluation strategy. Defines approaches to model quality governance, automated evaluation pipelines, and alignment of ML metrics with business KPIs at organizational scale.

Cargando comentarios...