技能档案

Python Programming

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 10 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		Writes clean Python code for data science: data processing functions, EDA scripts, simple ML pipelines. Uses list comprehensions, generators, decorators. Works with virtual environments and dependency management via pip/conda.
LLM Engineer		Knows Python basics: data types, functions, classes, modules. Writes scripts for LLM API interaction, text data processing, and basic ML task automation under mentor guidance.

角色	必要性	描述
Data Scientist		Develops production-ready Python code for ML systems. Applies OOP for structuring ML code, type hints for API documentation. Uses dataclasses, pydantic for data validation. Writes modular, testable code with proper error handling.
LLM Engineer		Independently develops in Python for LLM projects: async/await for API calls, dataclasses for configurations, generators for data streaming. Writes unit tests and uses type hints.

角色	必要性	描述
Data Scientist		Designs Python architecture for scalable ML systems. Optimizes performance through profiling, multiprocessing, async/await. Creates reusable packages and libraries for the data science team. Applies advanced patterns: metaclasses, context managers, descriptors.
LLM Engineer		Designs Python applications for LLM: high-performance async servers, memory-efficient data processing, custom PyTorch extensions. Optimizes performance through profiling and concurrency.

角色	必要性	描述
Data Scientist		Defines Python standards for the data science team: code style, architecture patterns, package management. Establishes shared Python infrastructure: internal packages, CI/CD templates, development tools. Conducts ML code architecture reviews.
LLM Engineer		Defines Python standards for the LLM team. Establishes coding guidelines, project structure templates, CI/CD pipeline. Conducts architectural reviews of ML system Python components.

角色	必要性	描述
Data Scientist		Shapes Python engineering strategy for data science organizations. Defines tools and best practices for scaling Python ML development. Evaluates emerging tools (Ruff, uv, Mojo) and plans adoption strategy.
LLM Engineer		Shapes enterprise Python strategy for ML/LLM organizations. Defines approaches to shared libraries, internal PyPI, and Python version management. Mentors leads on advanced Python techniques.

Junior 2 要求

Data Scientist

Writes clean Python code for data science: data processing functions, EDA scripts, simple ML pipelines. Uses list comprehensions, generators, decorators. Works with virtual environments and dependency management via pip/conda.
LLM Engineer

Knows Python basics: data types, functions, classes, modules. Writes scripts for LLM API interaction, text data processing, and basic ML task automation under mentor guidance.

Middle 2 要求

Data Scientist

Develops production-ready Python code for ML systems. Applies OOP for structuring ML code, type hints for API documentation. Uses dataclasses, pydantic for data validation. Writes modular, testable code with proper error handling.
LLM Engineer

Independently develops in Python for LLM projects: async/await for API calls, dataclasses for configurations, generators for data streaming. Writes unit tests and uses type hints.

Senior 2 要求

Data Scientist

Designs Python architecture for scalable ML systems. Optimizes performance through profiling, multiprocessing, async/await. Creates reusable packages and libraries for the data science team. Applies advanced patterns: metaclasses, context managers, descriptors.
LLM Engineer

Designs Python applications for LLM: high-performance async servers, memory-efficient data processing, custom PyTorch extensions. Optimizes performance through profiling and concurrency.

Lead / Staff 2 要求

Data Scientist

Defines Python standards for the data science team: code style, architecture patterns, package management. Establishes shared Python infrastructure: internal packages, CI/CD templates, development tools. Conducts ML code architecture reviews.
LLM Engineer

Defines Python standards for the LLM team. Establishes coding guidelines, project structure templates, CI/CD pipeline. Conducts architectural reviews of ML system Python components.

Principal 2 要求

Data Scientist

Shapes Python engineering strategy for data science organizations. Defines tools and best practices for scaling Python ML development. Evaluates emerging tools (Ruff, uv, Mojo) and plans adoption strategy.
LLM Engineer

Shapes enterprise Python strategy for ML/LLM organizations. Defines approaches to shared libraries, internal PyPI, and Python version management. Mentors leads on advanced Python techniques.

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