Skill Profile

Feature Engineering

This skill defines expectations across roles and levels.

Machine Learning & AI Classical Machine Learning

Roles

where this skill appears

Levels

structured growth path

Mandatory requirements

the other 5 optional

Machine Learning & AI

Classical Machine Learning

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		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.

Role	Required	Description
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.

Role	Required	Description
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.

Role	Required	Description
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.

Role	Required	Description
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 requirements

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 requirements

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 requirements

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 requirements

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 requirements

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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