8 Ways To Spot A Fake Data Scientist

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 onlyreal 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.