Undeniably<\/em>, data scientist<\/strong> jobs are extremely in demand and this position has become one of the most lucrative career options these days. As a result, recruiters and hiring managers are being flooded with applications. In this scenario, the expectation revolving around the perfect data scientist role<\/strong> candidate has changed to a great extent and businesses have started to understand the ability to train a machine learning model is just a small part of what it actually takes to be a successful professional in data science<\/strong>. So, what should you do to become that perfect data scientist role<\/strong> candidate? Let\u2019s have a look.<\/p>\n

1- Develop problem-solving skills<\/em><\/strong><\/h3>\n

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While<\/em> it\u2019s crucial for any data scientist<\/strong> to stay up-to-date with the latest tools and technologies, it\u2019s mandatory to have robust problem-solving skills. Assuming you\u2019ve got the required qualifications to become a data scientist<\/strong>, focus on developing this skill as much as you can. In reality, most businesses value experience much more than traditional educational qualification because with more experience, comes more ability to solve different business problems.<\/p>\n

2- Develop a solid focus on business impact<\/em><\/strong><\/h3>\n

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One<\/em> of the biggest factors that separate an average data scientist<\/strong> from a good one is the latter\u2019s natural curiosity to identify patterns in data. Try to delve into the detailed work of exploring datasets, explore the latest techniques in the data science<\/strong> field, test their effects systematically etc. Try to develop a mindset that would help you identify the larger goal of projects and question the core assumptions. The data science<\/strong> field is still not a standardized field and thus, there\u2019s much room for thinking outside of the box to solve pressing problems.<\/p>\n

3- Ability to manage expectation carefully<\/em><\/strong><\/h3>\n

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Data science<\/strong><\/em> can be quite a confusing field from the outside. It\u2019s almost impossible for those, who\u2019re not associated with this field, to understand whether a data science<\/strong> project is a machine learning project and things like that. The expectation around what a data scientist<\/strong> can do may greatly vary between different people. Thus, it\u2019s imperative for a data scientist role<\/strong> candidate to be able to consistently and proactively communicate with the stakeholders to identify clear expectations and find out misunderstandings early to get everybody on the same page.<\/p>\n

4- Ability to work with cloud services<\/em><\/strong><\/h3>\n

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Cloud<\/em> computing has already become a core part of the field of data science<\/strong>. There\u2019re lots of cases when a data scientist<\/strong> needs to use cloud services. These may include querying databases for scalable analytics, managing and sharing datasets, etc. The ability to work with major cloud service providers like AWS, GCP, Microsoft Azure etc is steadily becoming one of the preferred skills for businesses looking to hire data scientists<\/strong>.<\/p>\n

Conclusion<\/em><\/strong><\/h3>\n

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It\u2019s<\/em> certainly a great time to start your journey to become a data scientist<\/strong> if you aren\u2019t one already, but you\u2019ve to prove that you stand out of the pack. Focus on developing the skills mentioned above, put in real effort and you should be able to become a data scientist role<\/strong> candidate for hiring managers.<\/p>\n