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22 Differences Between Junior and Senior Data Scientists

What do experienced data scientists know that beginner data scientists don't know? Here is a quick overview.

Automating tasks. Writing code that writes code.
Outsourcing tasks to junior members or to consultants.
Managing people, hiring the right people, managing managers who report to you.
Training colleagues who might not be tech-savvy. Be an adviser for senior managers.
Identifying the right tools and assessing the benefits and minuses of vendor software and platforms, for a specific large-scale project (construction of a huge taxonomy, etc.)
Identifying the right algorithms and statistical techniques for a specific project. Blending these techniques as needed for optimal performance.
Not trusting data; identifying useful external or internal data sources, blending various data sources while cleaning data redundancies and other data issues.
Identifying the best features, perhaps using ratios or transforming, combining raw features to turn them into better predictors. Usually require a good understanding of the business you are in.
Understanding executive talk, and translating executive requests, questions, concerns, or ideas into successful data science implementations.
Measuring the ROI that you bring to your company; being able to convince executives about your added value (or providing sound explanations if ROI is negative or not perceived as positive, and offering a corrective path.)

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