Skill Profile

ML Model Evaluation

This skill defines expectations across roles and levels.

Machine Learning & AI MLOps

Roles

where this skill appears

Levels

structured growth path

Mandatory requirements

the other 10 optional

Machine Learning & AI

MLOps

2/22/2026

Choose your current level and compare expectations. The items below show what to cover to advance to the next level.

What is Expected at Each Level

The table shows how skill depth grows from Junior to Principal. Click a row to see details.

Role	Required	Description
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.

Role	Required	Description
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.

Role	Required	Description
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.

Role	Required	Description
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.

Role	Required	Description
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 requirements

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 requirements

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 requirements

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 requirements

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 requirements

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.

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