The world of Data Science can seem like a mystic and risky place if you are not very familiar with it. There is no clear definition of what exactly a Data Scientist is and what university degree a Data Scientist should have. Although in the past years, some universities started building specific master course in Data Science, there are many well established professionals that come from variety of different directions. It is more important what skills and experiences somebody entails, than what academic degree he or she has. On this page, we try to demystify and structure different archetypes of Data Scientists in order to help you answering the following question if you are searching a Data Scientist for your company:

• What kind of Data Scientist am I looking for?
• What skills must the candidates have and what tasks should they be able to master easily?

We did a lot of research and communication with industry partner in our Datageeks network and came up with four groups of Data Scientist profiles.

The Big Data Architectis the one who is able to master all your Tera or Peta bytes of data that are incoming on your server every seconds. The main task is to keep the distributed servers up and running even on high peaks of data flow. He or she is responsible for the whole architecture of your big data environment including data collection, stream and file storage, database systems and of course analysis as well as visualization tools. A modern architecture for Big Data applications might easily be build of 10 or 20 or more single components that all need to work smoothly together and might be outdated pretty soon.

Things a Big Data Architect should be able to explain blindfolded:
Hadoop, Cloudera, Hortonworks, MAPR, docker, Mesos, Spark, Yarn, Flink, Pig, Hive, Kafka, Elastic Search, Cassandra, Storm, Splunk, Kibana

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