Leveling Up to Data Scientist 3.0

Want to become the Junior, Senior, or Principal Data Scientist at your company? Learn what you must do to reach the next level of the career game in the world of Data Science.

There’s three levels on which businesses hire Data Scientists: Junior, Senior, or Principal. Whether you’re only beginning your career in Data Science or interested in changing careers to Data Science, you will find that you’re at one of those levels.

The Data Science Skills Matrix

Junior — Level 1.0

Junior Data Scientists need to have a burning passion for understanding algorithms in Machine Learning. You can show this passion by participating in open-source projects or kaggle competitions.

How They Work

How They Don’t Work

There’s a correlation between a data scientist’s ability to fully build our every part of a product and their ability to lead the team.

The Senior — Level 2.0

Good candidates for these roles have experience from past jobs in data science or related fields. They are also expected to have deeper knowledge of writing code and putting it into production.

Businesses prefer Senior Data Scientists since they offer tremendous value in exchange for a reasonable compensation. They have more experience than Junior Data Scientists, thus they don’t make costly greenhorn mistakes. Also, the company doesn’t pay them as much as Principal Data Scientists, but still requires them to create Data Science models that are production-ready.

How They Work

How They Don’t Work

The Level 2.0 Data Scientist is measured by the ability of their models to improve the business. This level of data scientist possesses a good intuition of how statistical models work and how to implement them. They are in the process of understanding how the company functions, but they aren’t expected to offer solutions to business problems yet.

The Principal — Level 3.0

How They Work

They have seen why products fail, and therefore they successfully drive new projects. They are valued contributors to discussions on products and enjoy teaching the firm about Data Science. With experience at providing impactful Data Science solutions, they are valuable assets to the Data Science team.

How They Don’t Work

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