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Focus on the top 20% of Data Scientists talent

We have developed a screening process to identify the best talent. We are experts in our fields and we are looking for great problem solvers with passion and drive - the types of people we want to work with (and learn from).

How do we screen?

1. online screening

We screen all data scientists on their CV and make a selection of the best profiles. We are looking for Data Scientists with a study background in econometrics, artificial intelligence, biomedical sciences, astronomy or mathematics. Our preference is for candidates with a master degree and at least 1 year working experience.

2. conversation

As soon as the Data Scientists come through the first round, we start a conversation. We test Data Scientists for their communication skills, feeling for the business and their seniority. We look for leadership skills, such as being able to manage the data science pipeline (from concepts to POCs, MVPs and upscaling).

3. assessment

The consultant is tested on their technical skills through an assessment. For this we work together with a company that specializes in online assessment in the domain of Data Science.

Three essential skills we are looking for

01. Business acumen

As a Data Scientist, it is important to understand the context of the project so that you understand the purpose for which you are going to work. You will have to ask the right questions to fully understand the business case. In addition to domain knowledge you need good communication skills.

02. Data Science skills

Now that you understand the context and purpose of the project, you need to translate the business need into a data science solution. You know which toolings are best for that and you are eager enough to learn new techniques, if necessary. An enterprising institution is desirable, because there is a good chance that you will have to invest in alternative resources.

03. IT Engineering skills

When you have found a model that may answer the business question, you need to understand how to translate this model into a functional solution. The model is built and integrated into the existing (or new) environment of the customer. You will need to be able to draw up the Data Pipeline and to think about a user interface.