The still fairly young profession of data analyst has generated a veritable boom in demand in recent years. At least that is what the current Hays Skilled Workers Index shows in its review of the past quarters. Specifically, the Data Scientist job description was a distant second in the fourth quarter of 2021. While only a few such job profiles were sought when the index was compiled in 2015 (100 points), the index value has now risen to 1,162 points. That means in 2021, more than 700 jobs were posted per quarter with this job description alone. Accordingly, companies are desperately looking for specialists who are familiar with the collection and structured processing of various data in order to develop reports for new specialist applications. But what exactly does such a freelance specialist do anyway? We asked freelance Data Scientist Olga Kostova about this.
IT Freelancer Magazine: Can you please tell us for which tasks exactly companies call you?
Olga Kostova: In the past, companies had only analog on-site locations. There, human interactions were relevant. Even if you’re just watching the guests, you can tell when they’re lost or annoyed – conversely, you can also see happy clients making your purchases smoothly.
Distribution via a website or app is still a black box for many, but you can serve thousands and even millions of users simultaneously, unlike an analog site. My tasks is to collect, structure and organize the data. I recommend and develop methods on how to use data sensibly, so that the data is useful and companies can use it to increase their efficiency. Data does not speak for itself. They also have no sense of priorities, which findings to act on and how urgently. This is where I come in.
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Data Science as a new profession – lack of human resources
IT Freelancer Magazine: Why do you think these companies don’t have the know-how themselves?
Olga Kostova: Data Science is so new that most people who are involved in it are actually trained for something else. Universities only slowly started offering specific programs for Data Science in 2016, at the same time that there was already demand from companies.
This discrepancy is one reason why the know-how cannot yet be available in the companies themselves; the human resources are lacking. But it’s not just talent that’s lacking; integrating Data Scientists into corporate workflows is also a challenge. This is a new department that must learn to work with others, while others have already had negative experiences or occasionally even fear losing their jobs due to data science automation. In the past, for example, there was a team that decided which products to produce and in what quantities – data science models can do that very well. In addition, “operational blindness” often occurs when the same data is always used.
As Zenmaster, Shunryu Suzuki said, “In the beginners mind there are many possibilities but in experts – there are few.” If you work with some data long enough, you no longer see anomalies and begin to suspect that previously recognized patterns, are no longer visible. It is an opportunity to create new perspectives. Another reason is the appropriation of new technologies and approaches.
The path to independence- a natural process
IT Freelancer Magazine: Which training path did you choose? Why did you decide to become self-employed?
Olga Kostova: I have been working in the industry for over 10 years. When I started, it wasn’t yet about Data Science. I studied history and probably wouldn’t have dared to switch to Data Science because it is commonly believed that history has nothing to do with it. But there are actually many parallels: One analyzes various texts, documents, agreements, correspondence, laws, extracts facts, opinions, prejudices and writes theses.
Self-employment was not what was in my mind. I was actually pretty scared of it. But then I thought of my parents, both teachers, who studied hard and chose the profession because it offered them very good prospects. However, when the Soviet Union collapsed, teachers became one of the lowest paid professions. So I realized that the security that comes with a permanent position is just an illusion. It was also the case that I grew naturally into independence.
In the beginning, there were digital products like websites or apps. The need to analyze user behavior to improve these products seemed and was, in the absence of competition, too low a priority. It was expected that developers, designers, or marketing teams could do the data analysis themselves. Thus, I began to outgrow such a team: due to my interest in analytics, statistical modeling and automation, which I additionally began to express by writing articles and giving talks at conferences, I went my own way and found myself in consulting and self-employment.
Understanding user behavior requires not only analyzing numbers, but also being able to verbalize user experiences into actionable points for business and technical teams. As the industry evolved with larger and larger amounts of data, I too had to evolve and learn new skills and qualifications step by step: Javascript, SQL, Python and more. Today I have not only technical knowledge, but also experience with brands such as Adidas, LEGO, Aida, XO Aviation, Vista and others.
Difference Data Scientists & Data Analyst
IT Freelancer Magazine: Is there a difference between the terms Data Scientists and Data Analysts? Which one?
Olga Kostova: There are many debates about this. Personally, I think the term “data analyst” is too generic to be a job title. Teachers, doctors, lawyers, engineers…. all analyze data. That’s why job/professional titles like conversion optimization specialist, growth hacker, measurement strategist, statistician, data scientist, etc. appeal to me, whereas when I hear a term like “data analyst” without context, I’m not even sure if the person knows what Excel or a database is. I don’t think this is just my opinion, because I often see on LinkedIn the tendency to include keywords like “Digital” or “BI”, “BigQuery”, “SQL”, “Python” next to the title Data Analyst. It looks to me like people are hesitant to call themselves Data Scientists: but they still specify what technologies they work with because the definition Data Scientist is just not specific enough.
Data, the gold of the 21st century- Industry-independent high demand for Data Scientists
IT Freelancer Magazine: Why has your profession become so attractive in recent years? Does this apply to certain industries more than others?
Olga Kostova: We are generating more and more data, digitizing more and more industries and processes, and every market is becoming more and more competitive – so the need for Data Scientists will only grow. At the same time, it’s a job you can do from your laptop, sometimes remotely – that’s very attractive to modern people.
I see a growing demand for Data Scientistsin the medical, engineering, agriculture, real estate and energy sectors. This is in addition to the already traditional high demand in IT, e-commerce, logistics.
Recommendation to companies: Train product managers with clientsbecome Data Scientists
IT Freelancer Magazine: What do you currently recommend to companies that are desperately looking for data specialists but can’t find any?
Olga Kostova: Companies need passionate product managers who put the clients first. You don’t need a statistical model to understand that when your customer is in Europe, they want to see prices online in euros, or that when they put two pillows and a blanket in a basket, they also need a couple of sheets. There is so much to be gained from pure logic and clients. My recommendation: Companies should first find people who understand the industry and the business, then ensure the data infrastructure (data acquisition, structure and quality)), and finally train people to become Data Scientists.
About the interviewee:
Olga Kostova is an experienced Data Scientist and conversion optimization specialist. Her specialty is the structuring of complex processes in the area of digital analytics and in marketing. Here, their goal is to achieve personalization, better user experiences, and meaningful impact on client revenue. To do this, it develops and implements data measurement plans to achieve maximum efficiency across its clients ‘ data science or product development activities. She has leadership experience and deep knowledge of presenting results to teams and executives.
“My main goal is to increase corporate profits in the most efficient way, and I think that’s the most challenging and exciting part of my job.”
