Using AI to identify AI talents
We have developed an AI driven 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).
Three main knowledge area's in a data science profile
1. Business Consultant
When a new businesscase is identified, the data science consultant should be able to receive and translate the requirements on the project.
2. Data Scientist
Based on this input, the Data Scientist should then be able to create a (new) data science model. Therefor he or she should have technical skills and knowledge of relevant toolings.
3. Infrastructure Engineer & UX Designer
When the model proves its (business) value, the model should be implementen in the (IT) infrastructure of the organization. Also a dashboard or interface should be build.
Top down vs bottom up approach
A business sensitive data scientist vs. a deep technical data scientist
There are typically 2 different profiles; a very technical profile and a more social business profile. The value of the profiles depends a lot on the circumstances of the project. In some cases, when you want to discover and test new businesscases fast, you prefer to have a business data scientist. In other cases, when you want to develop a large model over a longer period of time, a more technical profile is needed.
Technical skills we take in mind when screening a profile.
How do we screen?
1. online screening
With NLP techniques we screen all data scientists on their CV. 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. Also we evaluate the Github profile and we highly appreciate freelancers who builded data science models in their free time (those are the most passionated).
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. technical assessment
We have developed an automated python skill test to evaluate the structure of a data science model. Also, we use cognitive games to identify personalities of a freelancer.
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.