Skill Profile

Jupyter Notebooks

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		Works in Jupyter Notebook/Lab for EDA, model prototyping, and result visualization. Structures notebooks with markdown descriptions, creates reproducible experiments. Uses magic commands and extensions for productivity.

Role	Required	Description
Data Scientist		Effectively uses JupyterLab for the full ML cycle: from EDA to model evaluation. Applies papermill for parameterized notebook runs, nbconvert for report generation. Configures kernels for various environments and projects.

Role	Required	Description
Data Scientist		Designs notebook-based workflows for team data science collaboration. Integrates notebooks with MLflow, DVC, and CI/CD. Establishes notebook development standards: templates, code quality checks, reproducibility. Creates reusable notebook components.

Role	Required	Description
Data Scientist		Defines notebook development infrastructure for the data science team. Coordinates JupyterHub setup, resource and access management. Establishes processes for transitioning from notebook prototypes to production code.

Role	Required	Description
Data Scientist		Shapes interactive computing platform strategy for the organization. Defines enterprise notebook infrastructure: JupyterHub, Databricks, SageMaker notebooks. Evaluates cloud vs on-premise and security requirements for data science.

Junior 1 requirements

Data Scientist

Works in Jupyter Notebook/Lab for EDA, model prototyping, and result visualization. Structures notebooks with markdown descriptions, creates reproducible experiments. Uses magic commands and extensions for productivity.

Middle 1 requirements

Data Scientist

Effectively uses JupyterLab for the full ML cycle: from EDA to model evaluation. Applies papermill for parameterized notebook runs, nbconvert for report generation. Configures kernels for various environments and projects.

Senior 1 requirements

Data Scientist

Designs notebook-based workflows for team data science collaboration. Integrates notebooks with MLflow, DVC, and CI/CD. Establishes notebook development standards: templates, code quality checks, reproducibility. Creates reusable notebook components.

Lead / Staff 1 requirements

Data Scientist

Defines notebook development infrastructure for the data science team. Coordinates JupyterHub setup, resource and access management. Establishes processes for transitioning from notebook prototypes to production code.

Principal 1 requirements

Data Scientist

Shapes interactive computing platform strategy for the organization. Defines enterprise notebook infrastructure: JupyterHub, Databricks, SageMaker notebooks. Evaluates cloud vs on-premise and security requirements for data science.

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