Skill Profile

Time Series Analysis

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		Understands the fundamentals of Time Series Analysis. Applies basic practices in daily work. Follows recommendations from the team and documentation.

Role	Required	Description
Data Scientist		Applies advanced time series methods: Prophet, SARIMA, VAR, state space models. Conducts feature engineering for temporal data: lag features, rolling statistics, Fourier features. Uses cross-validation for time series (TimeSeriesSplit).

Role	Required	Description
Data Scientist		Designs production time series forecasting systems. Applies deep learning for time series: N-BEATS, Temporal Fusion Transformer, DeepAR. Implements probabilistic forecasting with quantile regression. Works with multivariate and hierarchical time series.

Role	Required	Description
Data Scientist		Defines forecasting strategy for the data science team. Establishes reusable forecasting framework and evaluation standards. Coordinates forecasting platform development for business users and automated systems.

Role	Required	Description
Data Scientist		Shapes forecasting and temporal analytics strategy at organizational level. Defines investments in forecasting infrastructure, evaluates foundation models for time series. Publishes methodologies and shapes thought leadership.

Junior 1 requirements

Data Scientist

Understands the fundamentals of Time Series Analysis. Applies basic practices in daily work. Follows recommendations from the team and documentation.

Middle 1 requirements

Data Scientist

Applies advanced time series methods: Prophet, SARIMA, VAR, state space models. Conducts feature engineering for temporal data: lag features, rolling statistics, Fourier features. Uses cross-validation for time series (TimeSeriesSplit).

Senior 1 requirements

Data Scientist

Designs production time series forecasting systems. Applies deep learning for time series: N-BEATS, Temporal Fusion Transformer, DeepAR. Implements probabilistic forecasting with quantile regression. Works with multivariate and hierarchical time series.

Lead / Staff 1 requirements

Data Scientist

Defines forecasting strategy for the data science team. Establishes reusable forecasting framework and evaluation standards. Coordinates forecasting platform development for business users and automated systems.

Principal 1 requirements

Data Scientist

Shapes forecasting and temporal analytics strategy at organizational level. Defines investments in forecasting infrastructure, evaluates foundation models for time series. Publishes methodologies and shapes thought leadership.

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