But what to measure and how? Why is it important? Where to find the data? How to gain a better understanding of where your business is and where it needs to be?
Enter the data scientist.
Without this increasingly important position, businesses would struggle to make sense of vast amounts of data they are producing and gathering. Why are customers not coming back? Why are deliveries taking longer? Why are our customer satisfaction scores lower? All questions that can only be answered by looking at the data by someone who knows how.
This is why data scientists, and all roles dealing with big data, are the newest “it” job on the tech job market, not least in our own hub, Berlin. There is a keen interest in specialists that are able to manage and make sense of big data, and turn it into actionable business strategies. No matter the size of the company, or the industry, a good data scientist is gold.
What does the work of a data scientist look like? The first step is to identify the problem with relevant stakeholders. This usually involves counterparts from the business, tech and, if this is separate, a data/business intelligence department. The next step is data acquisition, gathering data from numerous sources, such as servers, logs, databases, APIs, online repositories, etc. Next is data preparation, which involves cleaning and ensuring consistency of data. The natural further step is transforming, modifying and manipulating the data with the business problem in mind. Exploratory data analysis, a crucial following step, includes selecting relevant variables that will be used in model development. This is the most important step: identifying the right data and variables to create the right model to tackle the business problem at hand. Following testing models on sample datasets to ensure their validity, the final step is to communicate and visualize the findings to the relevant business stakeholders.
A data scientist is of course not the only position in the realm of data science, though, along with the data analyst, perhaps the most self-explanatory. There are many different positions that represent different branches of data science. Machine learning engineers represent a very interesting and very topical sub-domain of data science. They are computer programmers, but ones that program computers to perform tasks that they were not necessarily programmed to do. For example, they are responsible for autonomous automobiles being able to almost independently get from point A to B in an efficient and safe manner. Data engineers are also important to mention, and to differentiate from data scientists. Engineers are individuals responsible for designing, integrating, organizing data from all sources, in a way that it can be used by data scientists. In essence, they prepare the groundwork that makes data scientists lives and jobs easier.
In terms of employment models, freelance data scientists are an option that we have seen an increased interest in amongst our partners, both on the employer and employee side. Companies are looking for a fresh perspective, without the financial burden of hiring a traditional consulting company. On the other hand, data scientists are also interested in freelance opportunities, to explore different facets of data science, to challenge themselves in different industries and with a greater variety of problems.
At expertlead, tech talent is evaluated and matched with challenging projects. Through a process which includes an interview about the candidate’s previous project experience, an in-depth technical session with a senior data scientist from the company’s network and continuous review of client and peer feedback, both the quality of the tech community is ensured on the one hand and the success of clients’ projects on the other.
Want to find out more? Get in touch with expertlead!