Jobprofil

Data Scientist (m/w/d)

"We are generating more and more data, digitizing more industries and processes, and markets are becoming more and more competitive - so the need for data scientists is only going to grow." - Olga Kostova, data scientist and conversion optimization specialist"

In a highly digitized world, data represents immense value. It is therefore important for companies from all industries to handle this data intelligently and draw the right conclusions from it. To achieve this, they often call on the help of experts: data scientists. This relatively new professional field is more in demand today than ever before, because data scientists know exactly how to process, organize and read data and how to derive forecasts from it.

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► Definition
► Salary
► Tasks & activities
► Training, studies & further education
► Skills
► FAQ

Data Scientist Definition: What is a Data Scientist?

Data scientists clean and analyze data, systematically evaluate it and extract valuable information from a huge amount of data.
Data scientists use the insights they gather to advise the management of their companies and thus help them to achieve strategically sensible goals more effectively. They can make forecasts or issue warnings (e.g. by analyzing all returns or complaints) and thus give their organization a major competitive advantage. Without this analysis and processing, companies are often left in the dark with their existing data volumes.
The demand for data scientists and the use of big data has therefore risen sharply in recent years. Data growth, and with it the ever-increasing need to track and analyze data volumes, is growing exponentially.

Olga Kostova herself has been working as a data scientist freelancer for many years and describes the high demand for data scientists as follows:

"In the past, companies only had analog locations on site. Human interactions were relevant there. Even if you just observe the guests, you can tell when they are lost or annoyed. Sales via a website or app is still a black box for many. (...) My job is to collect, structure and organize the data."

Data scientists can work in a wide variety of industries, including: Internet services and social media platforms, insurance companies, online retail, banks or in research.

Data Scientist salary: What does a Data Scientist earn in Austria?

On average, a data scientist in Austria earns a gross salary of around €54,800 per year.

As in many other professions, the salary depends on various factors: the industry, the size of the organization as well as the experience you have gained and your own skills. Therefore, the salary increases continuously the more professional experience you have gained over the years.

Starting salary as a data scientist in Austria: Junior data scientist salary

Junior data scientists in Austria start their careers with a starting salary of around €50,000 per year. After three to six years of professional experience, this salary rises to around €60,000.

Senior Data Scientist Salary

Senior Data Scientists with more than 10 years of experience earn a gross salary starting at €70,000 per year, although this can increase significantly depending on the employer and additional qualifications.

What does a data scientist do? Tasks and activities

A data scientist identifies and analyzes structured and unstructured data sources and volumes. He or she recognizes patterns, checks them and can make deductions from them, which are then passed on to the responsible persons. Tracking and monitoring the various data sources is also one of the tasks of this person.

Data scientists are constantly developing new analytical methods in their work in order to perfectly analyze the existing and extensive database and implement their requirements well. As large as the amount of data is, so is the information potential that the data scientist can extract. The available data is then linked and interpreted using various big data techniques.

In addition, a data scientist uses advanced analytics, a process for data processing that goes beyond the classic evaluation and visualization of data (business intelligence). This advanced analysis can also be used to make predictions about the future. With predictive analytics, data scientists can determine what effects certain changes (could) have.

This process turns big data into smart data.

Experts in data science are in demand to gain insights for management, who use these insights to make current and future business decisions. Their most important areas of responsibility include Big data, data engineering, data mining, smart data, machine learning and predictive analytics.

Specifically, a data scientist has the following tasks:

Collection and evaluation of data from various relevant data sources
Checking the data for accuracy, relevance and traceability
Descriptive analysis of the collected data and connection to various databases (data warehouse or data lake)
Presentation of own ideas and presentation of successful use cases
Creation and validation of machine learning models
Contact for domain experts for data science

In smaller companies in particular, there is often only one person responsible for the tasks presented here. However, the larger the company, the more likely it is that the tasks will be distributed among several data scientists, each with a different specialization in dealing with big data. Entire data science teams are therefore not uncommon.

Junior Data Scientist tasks

Collecting, cleansing and analyzing data and presenting results. As a Junior Data Scientist, these are also your tasks. At the start of your career, you may join a team of data scientists and provide support with requirements analysis, data preparation and presentation.

In order to make the step from Junior Data Scientist to Senior Data Scientist, it is advisable to familiarize yourself with the various areas of Data Science. A wealth of knowledge and additional strategic thinking will have a positive impact on your career path in this field.

Senior Data Scientist tasks

The work of a senior data scientist with several years of experience is often more project-based and strategic. In contrast to Junior Data Scientists, who take care of assigned tasks, their more experienced colleagues often oversee entire projects. They take care of the distribution of tasks and the time schedule. An important skill here is the correct prioritization of individual tasks in order to achieve good results.

Becoming a data scientist - training, studies & further education

Due to the strong demand for data scientists, there are now various training opportunities to become a data scientist. The usual way to become a data scientist is through a suitable degree program. More and more universities are offering degree programs in data to meet the demand.

There is also the option of taking the path towards data science via certificate courses and further training.

Data science is a comparatively new field, which means that there is often not yet enough know-how in companies. Olga Kostova says: "Data science is so new that most people working in it are actually trained for something else. Universities only slowly started to offer specific programs for data science in 2016, at the same time as there was already demand from companies. This discrepancy is one reason why the know-how is not yet available in the companies themselves; there is a lack of human resources."

Data scientist training

There is no classic data scientist training or data scientist apprenticeship in Austria. In principle, a degree in data science, computer science or other relevant fields is required for this work.

There are numerous seminars and further training courses that can be taken online or in person to build up even more professional knowledge after graduation.

Another option for training as a data scientist is a six-month diploma course that provides in-depth training for working as a data scientist. This includes, for example, the "Data Engineering and Artificial Intelligence" course at Coders.Bay Vienna, where the costs are also covered by the Public Employment Service.

Data Scientist studies

A data science degree enables direct entry into a career as a data scientist. However, other university degrees, such as in physics, computer science, statistics or other STEM subjects, also provide a solid basis for familiarizing yourself with the subject area both theoretically and practically.

There are numerous universities in Austria that offer a Bachelor's or Master's degree program in Data Science. These include, for example, the Bachelor in Data Science at Modul University in Vienna, the Bachelor in Industrial Data Science at the University of Leoben and the Master's degree in Data Science at the University of Salzburg, to name just a few.

During your studies, you should ideally acquire skills and knowledge in the following areas or attend courses and lectures in these areas:

Data Management
Entrepreneurship
Information Design & Communication
IT
Data analytics

Data Scientist further training

Due to the increasing popularity of this professional field, there are also more and more data scientist training courses on the market. Various institutes, such as the BFI or WIFI, offer online seminars or face-to-face training in data science and big data or advanced areas, which can be completed with a certificate. Possible fields in which you can continue your education are Programming languages, database models, development standards, cloud technology, data protection and much more.

Data Scientist lateral entry

Various online courses, seminars and further training courses offer a good starting point for both entry-level and lateral entry as a data scientist. Here, too, it is advisable to have previously completed a degree in an economics or IT subject. This is because most employers require a degree to fill this position.

Skills of a data scientist

Your technical skills as a data scientist are an important success factor for your future career. In addition to a strong understanding of mathematics and statistics, you should also have programming skills - especially in Python and R - and a basic (technical) understanding of software development and data analysis.

In addition, you should acquire knowledge of machine learning, data assimilation, business analytics or applied data science.

The most important hard skills of a data scientist are:

The most important hard skills of a data scientist are:
Extensive knowledge in the field of big data
Good math skills
Strong understanding of statistics
Programming skills, especially in Python and Java
Sound knowledge of SQL databases
Understanding of artificial intelligence and machine learning
Dealing with data management tools, such as Hadoop

In addition, as a data scientist, you should also stand out with your own soft skills, as you often come into contact with people in this profession.

The following soft skills therefore play an important role in your professional success:

Strong interest in new trends and technical developments in the field of big data, data science and business intelligence
Strong self-motivation, creativity and analytical skills
Ability to work in a team, intercultural knowledge and business fluency in English
Persuasiveness and stress resistance

Customer centricity via data analysis

Due to the very high demand, it is not always easy for companies to find qualified data scientists. We ask Olga Kostova what she advises companies that are urgently looking for data scientists:

"Companies need passionate product managers who put the customer at the center. You don't need a statistical model to understand that when your customer is in Europe, they want to see the 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 customer orientation. My recommendation: companies should first find people who understand the industry and the business, then ensure the data infrastructure (data collection, structure and quality), and finally train people to become data scientists."

Although data scientists are a great asset to a company, it is important to understand the customer first. Some of the things that data scientists find out after lengthy analysis could also be determined by product managers using pure logic.

Data Scientist Career: Opportunities on the job market as a data scientist

Your chances as a data scientist on the (Austrian) job market are extremely good. Hardly any other industry has seen such strong development over the last few decades as the field of data. The number of specialists in Austria can hardly keep up with this demand, which is why you have good prospects as a qualified data scientist with a degree.

For data scientist Olga Kostova, the trend is clear: "The need for data scientists is only going to grow. I see a growing demand for data scientists in the fields of medicine, mechanical engineering, agriculture, real estate and energy. This is in addition to the already traditionally high demand in IT, e-commerce and logistics."

If you are interested in a career in data science, now is the right time to find out more and look into the job profile of a data scientist. Because demand is not going to decrease for the time being.