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challenges in building a data science team

3 Common pitfalls in building a Data Science team

by Petr Paščenko, Head of Data Science

In the last few years, I have seen several attempts to build a good data science/machine learning team both at Profinit as well as with our clients. Over time, I have observed several notorious anti-patterns, which tend to be very common in the field. Here are the three most noteworthy examples of such pitfalls:…

automated data science

Will Data Science become automated?

by Dominik Matula, Data Scientist

Full disclaimer: the author works as a data scientist and loves his job. Therefore, once robots take over the field of Data Science, he might lose his job and spend his last days being depressed. Or will he? Let’s not beat about the bush, looking back in time every profitable…

data science job market tips

4 Facts about the Global Data Science Job Market

by Barbora Fuksa, Business Development Manager

1. Employers are often looking for a unicorn data scientist Everyone working in the field is well aware that the skill set a data scientist brings to the job can vary widely depending on the context. A combination of statistical background, coding knowledge, and business acumen are definitely required, but to find an individual that…

instalment detector - data science product for banks

Data is the new oil

by Dominik Matula, Data Scientist

Have you ever tried digging deeper into your company data warehouse? Perhaps you found one or two skeletons in the cupboard, but maybe also a hidden golden vein. If you haven’t, then it’s about time you roll your sleeves up and start the data mining! We live in the age of information and it has…

big data and ai summit in berlin 2019 review

Big-Data.AI Summit 2019 in Berlin

by Barbora Fuksa, Business Development Manager

Our review of the most prominent event about Big Data and AI in Germany.

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