Skill Profile

Pandas / NumPy

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 with pandas DataFrame for loading, cleaning, and analyzing data. Performs basic operations: filtering, grouping, aggregation, merge/join. Uses numpy for mathematical operations and array manipulation. Builds pivot tables and descriptive statistics.

Role	Required	Description
Data Scientist		Efficiently works with large datasets via pandas: chunked processing, dtype optimization, multi-index. Applies numpy vectorized operations for high-performance computing. Uses pandas profiling for automated EDA.

Role	Required	Description
Data Scientist		Designs high-performance data processing pipelines with pandas/numpy. Applies advanced techniques: Cython extensions, numba JIT compilation for numpy. Optimizes memory footprint through chunked processing and memory-mapped arrays for out-of-core computing.

Role	Required	Description
Data Scientist		Defines data handling standards for the data science team. Establishes shared data processing utilities and best practices. Coordinates migration from pandas to distributed frameworks (Dask, Polars, Spark) when needed.

Role	Required	Description
Data Scientist		Shapes data processing stack strategy for the organization. Defines when to use pandas vs Polars vs Spark for different scales. Evaluates emerging tools and their fit for the organization's data science workflow.

Junior 1 requirements

Data Scientist

Works with pandas DataFrame for loading, cleaning, and analyzing data. Performs basic operations: filtering, grouping, aggregation, merge/join. Uses numpy for mathematical operations and array manipulation. Builds pivot tables and descriptive statistics.

Middle 1 requirements

Data Scientist

Efficiently works with large datasets via pandas: chunked processing, dtype optimization, multi-index. Applies numpy vectorized operations for high-performance computing. Uses pandas profiling for automated EDA.

Senior 1 requirements

Data Scientist

Designs high-performance data processing pipelines with pandas/numpy. Applies advanced techniques: Cython extensions, numba JIT compilation for numpy. Optimizes memory footprint through chunked processing and memory-mapped arrays for out-of-core computing.

Lead / Staff 1 requirements

Data Scientist

Defines data handling standards for the data science team. Establishes shared data processing utilities and best practices. Coordinates migration from pandas to distributed frameworks (Dask, Polars, Spark) when needed.

Principal 1 requirements

Data Scientist

Shapes data processing stack strategy for the organization. Defines when to use pandas vs Polars vs Spark for different scales. Evaluates emerging tools and their fit for the organization's data science workflow.

Loading comments...