Data Scientist: Hype or Sexy?

Data Scientists seem to be everywhere nowadays. This title has seen a huge increase in appearences in job descriptions, as Indeed.com demostrates in its data.
There are several sites and articles that even describe the job as sexy:

The combination of handling Big Data and Analytics is what makes this title so attractive. So far handling data and analytics were too parts, sometimes combined in one person, but most times not. But with all the unstructured data available and new tools to handle it, the combination is easier to handle for one person. But technical understanding is not enough to become a Data Scientist. It requires an understanding of products and customer behaviour as well as how to manage and analyse data.
Right now there is no programm that graduates with the title Data Scientist, so companies have to look for people that learned all skills during their career so far or have strong affinities towards analysis or programming with corresponding skills learned during their studies or education.
Because of the interdisciplinary area of this field, some companies try to get their hands on graduated physicist. Their studies involve a great deal of interdisciplinary themes and the affinity to both algorithms and research.
Other possibilities are Business Intelligence Analysts, Data Mining specialists or even Web Analytics manager, depending on their career so far, especially their experience with different kinds of data and their presentation skills regarding actions resulting from their analysis.
All in all this new title and the news coverage it is getting is a great opportunity for people already working in this field and newbies wanting to work with both data and analysis.

This makes this new profession sexy.

Author

  • Marc Matt

    Senior Data Architect with 15+ years of experience helping Hamburg’s leading enterprises modernize their data infrastructure. I bridge the gap between legacy systems (SAP, Hadoop) and modern AI capabilities.

    I help clients:

    • Migrate & Modernize: Transitioning on-premise data warehouses to Google Cloud/AWS to reduce costs and increase agility.
    • Implement GenAI: Building secure RAG (Retrieval-Augmented Generation) pipelines to unlock value from internal knowledge bases using LangChain and Vector DBs.
    • Scale MLOps: Operationalizing machine learning models from PoC to production with Kubernetes and Airflow.

    Proven track record leading engineering teams.


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