Data science is one of the fanciest jobs of the decade and there are a lot of people who are looking to call themselves data scientists even if that means they do not have the actual skills. However, it makes hiring data scientists a tedious job as there is no shortage of fake resumes floating around who are looking to get the role. The fraud also stems out from the fact that the job descriptions are not properly understood. This makes many people think that they are data scientists — just because they deal with data. 

To keep away from fake data scientists and hire only real data scientists, it is important for recruiters to be educated about the difference between roles like data scientist, data analyst, data engineers and others. It is also important for them to ask the right questions and keep an eye on some of the points discussed below to spot a fake data scientist. Here are some pointers: 

1| If the candidate doesn’t have an advanced degree: Data science requires a sound knowledge of technical skills which comes with a sound background in technology. It is observed that most genuine data scientists have at least a Masters or a PhD degree to lead data science roles in organisations. A true data scientist is expected to have a sound quantitative, technical and scientific knowledge, which lacks in a fake data scientist. A fake data scientist may not have a strong foundation in a technically rigorous program and may have knowledge of just a few tools. 

2| If the candidate doesn’t have experience in statistical analysis: Apart from a strong technical knowledge, a true data scientist is expected to have a strong eye for statistical analysis. They would have ideally worked with unstructured data, organising and structuring large data sets. If a candidate has little or no experience of statistical concepts analytics, they may be data engineers but not a data scientist. 

3| If the candidate doesn’t have results and use-cases to show: Data science is not just about posing knowledge about tools and techniques. Much more than the knowledge of specific programming, a true data scientist should be able to effectively use this knowledge to solve problems. This can be identified by asking them questions about how they worked with a specific problem, the approach they used and what were the outcomes of their approach. Asking specific questions about use cases will help to identify true data scientist rather than asking if they know Python or Hadoop.

4| If the candidate is not a problem solver: Problem-solving and analytical abilities are the must-have skills for data scientists. If a candidate fails to shows these skills during the interview process, they are not true data scientists. Data scientists go about problem-solving in a specific way which can be used as a way to judge how a person thinks and acts. 

5| If the candidate doesn’t have business skills and acumen: We have heard and written about it multiple times that data science is not just about technical knowledge but requires strong business acumen. While a candidate may have skills such as Python, SPPSS, Excel or R if they are not able to use their analysis and findings to accomplish business goals. A true data scientist knows how to present findings and insights to the C-suite in a way they can understand. Without strong business-savvy skills, they are fake data scientists. 

6| If the candidate has more academic background than industry exposure: It may not be the case always, but if a candidate has a strong and long hold in academic and research background, they may not make for a good corporate data scientist. The work cultures of the two set-ups are entirely different and while one may be able to find insights, delivering it to leaders is a skill that a true data scientist should have. Research academics may have data skills but may lack business skills, which is a key requirement for a data scientist. 

7| If the candidate is not asking the right questions: The type of questions and interactions that happen during an interview can indicate if the candidate is genuine or not. A good data scientist might want to ask you questions about the company, how data is collected, team structure, budget of the company to tools and software, and more. Fake data scientists may not be well equipped to come out with such specific questions. 

8| If their social networking lacks a connection with fellow data scientists: While this is not a crucial point, it may be one of the key indicators of whether a candidate is a genuine data scientist or not. It is only natural for a data scientist to be connected to fellow data scientists on social networking sites such as LinkedIn, but they have alarmingly low connects in the field, they may be posers.

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