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Jobs in Data – Bye bye Unicorns 

In the past, they were the unicorns – the data scientists. Today we have teams with very different types of roles working in the fields of data and AI because this is not any more only a topic for IT but also for others. Not an easy job for HR to find the right people for this 🤯!

There are a variety of jobs in data beyond the role of a data scientist. Here are a few examples:

Data Analyst: A data analyst collects, processes, and performs statistical analyses on data to uncover insights and make recommendations. They are responsible for cleaning, transforming, and modeling data to provide insights to the business.

Data Engineer: A data engineer is responsible for designing, building, and maintaining the infrastructure that enables data processing and analysis. They ensure that data is accessible, scalable, and secure.

Data Visualization Developer: A data visualization developer creates interactive data visualizations and dashboards to help businesses understand and to show opportunities for revenue growth based on sales, customer, and industry data.

Data Strategist: The job of a data strategist is to develop and implement strategies that help organizations make the most effective use of their data. This involves working with various stakeholders to identify business objectives and goals that can be supported by data, and then developing a plan to collect, analyze, and use that data to achieve those goals.

Industry Expert: An industry expert will use their understanding of the industry and its trends to identify patterns and insights within the data. They may be responsible for collecting, organizing, and analyzing data from various sources, such as surveys, research studies, market reports, and industry publications. They may also develop models or predictive algorithms to help forecast future trends and identify potential risks or opportunities.

We could name and describe more data jobs (like Data Architects, Data Governance Lead, BI Analysts, etc, etc, etc), but that would go beyond the scope of our newsletter. We’d rather ask the question, “What makes a good data team?” 👀

“What makes a good data team?” This certainly includes hard and soft factors such as:

Different skills, expertise and experience: A good data team should be composed of individuals with diverse skills, expertise, and experience. Diverse teams are more likely to recognize their biases and solve problems when interpreting data, testing solutions, or making decisions. Each team member should bring a unique perspective and be able to contribute to solving complex data problems.

Clear goals and objectives: A good data team should have a clear understanding of the business goals they are trying to achieve using data. They should work closely with other departments in the organization to ensure they are aligned with the overall business strategy.

Strong communication skills: A good data team should be able to easily communicate complex data concepts to non-technical stakeholders. They should be able to present data findings and recommendations clearly and concisely.

Agile methodology: a good data team should be able to work in an agile and iterative manner. This means it should be able to quickly prototype and test hypotheses and make adjustments as needed based on feedback.

Data governance and security: a good data team should be committed to maintaining the integrity and security of data. It should follow industry best practices for data governance and ensure that data is stored and processed securely.

Die DATA Party geht weiter!
Das DATA festival #online kommt am 19. und 20. November zurück!
Zwei Tage lang dreht sich wieder alles um die nutzbringende Anwendung von Daten & künstlicher Intelligenz und wie wir damit unsere digitale Zukunft gestalten können. Die Teilnahme ist kostenlos.

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