技能档案

Feature Engineering

此技能定义了各角色和级别的期望。

Machine Learning & AI Classical Machine Learning

角色数

包含此技能的角色

级别数

结构化成长路径

必要要求

其余 5 个可选

Machine Learning & AI

Classical Machine Learning

2026/2/22

选择当前级别并对比期望。下方卡片显示晋升所需掌握的内容。

各级别期望

表格展示从初级到首席的技能深度变化。点击行查看详情。

角色	必要性	描述
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.

角色	必要性	描述
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.

角色	必要性	描述
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.

角色	必要性	描述
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.

角色	必要性	描述
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 要求

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 要求

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 要求

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 要求

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 要求

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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