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The field of data science is an extremely competitive market, especially if you want to work for one of your favorite tech companies. The good news is that you have the ability to gain an advantage in such a position by being prepared for anything. When you land your dream job, make sure to avoid the mistakes that data scientists make early in their careers.

Here are five data science career blunders that everyone should avoid:

Instead of a thought partner, be an infantry soldier:

People, especially juniors, are expected to sit back and not contribute their thoughts to the decision-making process, and then work on whatever decision has been made for them. While it may feel good at first, stakeholders may not invite you to future meetings, and you will have fewer opportunities to contribute. You should try to avoid this as a data scientist. Participate actively in discussions to maximize your impact.

Investing in a specific area of data science:

Often, data scientists are only concerned with building models and are unconcerned about any business aspects related to modeling, whereas data engineers are only concerned with data pipelines and are unconcerned about the modeling taking place in the organization. As a result, your personal and professional growth as a data scientist is hampered. Make an effort to learn about other areas of data science as well.

Failure to keep up with current events:

The most common mistake people make is becoming too comfortable with their data abilities and failing to make the effort to learn new ones. Because data science is still a young field undergoing rapid change and advancement, doing so is riskier than in other areas. Every day, new computations and devices, as well as innovative programming languages, are introduced.

Excessive analytical flexing:

You should cut your dress so that it fits your clothes, according to the verse. As a data scientist, you must follow the same rules when working with machine learning. It will be exciting at first to apply all of the fancy models you learned in school to solve all of the real-world problems. Instead of academic research, this 80/20 rule is always in play in the real world. Take your time learning about the company, its stakeholders, and the world.

Someone else will construct a data culture:

Don't believe that changing the data culture is someone else's responsibility. Accept responsibility for bringing about change. In the end, who better to build the data culture and teach data partners than data scientists? Your life will be made much easier if you contribute to the development of organizational data culture.

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