Job portals are flooded by adverts and recruiters are fighting to headhunt the most talented candidates. According to LinkedIn, statistical analysis and data mining are some of the top skills in 2017. We should not be surprised by this growing demand, companies now generate a huge amount of data; It can be a struggle to make sense of it.
And still it is quite difficult to find candidates with knowledge of different areas within data science. At SalesBI , we look for versatile candidates who are interested in developing multidisciplinary skills ranging from an intersection of mathematics, statistics, computer science, communication and business.
Data science is all about solving problems. The strongest data scientists are motivated by a curiosity to explore data in very creative ways to solve these business problems. But to be a good scientist you will also need a good understanding of the industry you are working in. You should know what business problems your company / customer is trying to solve, how the solution will deliver value, and how it will be used by whom. And want more, to be a good scientist, you need to think creatively, and innovate. The scientist should be eager to learn more, and think out of the box.
Data scientists wear multiple hats, and for us the most important one is to have strong business skills. We need our employees to understand data at a glance and equally importantly to understand what is going on in the business world as a whole. The data scientist needs to have in-depth understanding of business requirements and be able to effectively communicate with people at all levels of an organization.
The data scientist needs to understand statistics and applied mathematics, and they need to have a kind of mathematical intuition. They need to be able to understand and use proper machine-learning models and apply them to particular projects. A good data scientist tests their hypotheses with custom designed experiments.
The best data scientists know enough to engineer processes for sourcing, processing, and storing their data. And they communicate their findings through data visualizations and stories. So to be a data scientist, you should have your hands on a number of tools and technologies. Some of the languages and applications you can use are SQL, R, Python, SPSS, Tableau, and Hadoop. In our company we are not tied down by technology so neither should our data scientists. We provide customers with technology agnostic solutions to ensure our business intelligence solutions work for them and the platforms they use. Which means that scientists should be able to use a diverse tool to help them provide the best solutions.
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