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A Complex Job
Data Scientist, a complex job. The digital sector has greater mobility than the others, thanks to the fact that the same technologies are applied in a fairly similar way from one industry to another.
It is therefore easy to change jobs, but in the case of Data Scientists the turnover is impressive, and the reason lies in the difficulty of what is asked of him.
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Data into Profit
Every company has at its disposal a huge amount of unstructured data, in separate silos, processed for different reasons, and the hope of mixing it all with a sprinkle of artificial intelligence, thanks to the gurus who are the data scientists, is often misplaced.
The dream of transforming this data into profits never takes into account analysis and time required. Quality, noise and reliability of sources over time require a long preparation. It often happens that these professionals find themselves doing much trivial and boring things, such as cleaning up data and formatting time series.
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Complexity of the Work
In addition to the organizational aspect, in order to be able to keep these professionals in the company for at least 5-6 years so that they add value, the technological aspect also comes to the rescue of the complexity of the work required.
New no-code tools arrive on the market for the classification and statistical analysis of data, and also for the robotic development of code in Python and R in order to relieve these professionals of the most repetitive tasks.
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Training Models
The no-code and low-code approaches are really important in the company: on the one hand they allow "business" colleagues to be increasingly independent in the creation of reports and statistical analyzes, on the other hand they help digital staff to increase their productivity and quality of the algorithms they develop.
Data Scientists need no-code tools that deal with the collection, cleaning, quality control of data, and the creation of artificial intelligence training models.
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Natural Language
Tools that, by independently testing which machine learning technique is best suited to the problem, to research the key parameters and also to evaluate the model, allow the Data Scientist to concentrate on creating the model.
Recently, solutions have emerged that can process, understand and even generate natural language, or communicate in common English or Italian, without having to run into lines of software code.
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New Scenarios
If until yesterday we were talking to Alexa to turn on the light or look for a piece of music, now we are at the point of asking these tools to help us in the creation of artificial intelligence models.
Think of the advantage of being able to talk to a machine and arrive together with a forecast model of prices on the market.
This scenario is not a distant future, but tomorrow, and to better prepare you need to build a team where Data Scientists and other professionals focus only on business problems.
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