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Data scientist: missions, studies and salary

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Today, the profession of data scientist generates growing interest, both on the part of workers wishing to train in the subject and on the part of companies who see an obvious opportunity in data.

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In this regard, a recent “Social Trends” survey carried out by Malt in 2019 showed that data scientists represented the third most sought-after profile by large companies.

The growth of this profession is directly linked to the challenges of knowledge and mastery of big data: companies are required to record, manage and analyze increasingly large volumes of data in order to optimize their marketing positioning by example. Here is everything you need to know about the data scientist.

The missions of the data scientist

A data scientist, like any profession, involves recurring missions, regardless of the positions offered and working conditions.

Define and develop statistical models

First of all, the data scientist must rely on indicators and algorithms to structure the data in a relevant way. As a result, he comes to create statistical models for the company in order to systemically extract customer knowledge from raw data.

Thus, the data scientist automates and deploys large-scale structuring means, in order to process considerable volumes of data in optimal time. He thus represents a vital time saving for the company which employs him.

Study the data

On the other hand, the data scientist, after having developed his statistical models and structured the data, must:

  • Collect.
  • To select.
  • Analyze.

Of course, the data scientist must ensure that the data he uses and interprets is relevant to the situation of the company and its environment. He must therefore ensure that this data is of high quality and consistent with the objective set by the company in terms of customer knowledge.

In terms of consistency, it is also responsible for ensuring that the data collected in all the raw data always remains relevant and that its statistical models have been effective.

Synthesize the results obtained

Once the data has been sorted, filtered and analyzed, the data scientist gets to the very essence of his job, and what constitutes his added value for the company: reporting. In other words, he must return the results of his analysis in a clear and understandable way to his management, or even to all employees in the company.

This restitution of results can take many forms, both in terms of oral presentation and webinar or even a written report to be distributed internally to the employees concerned.

Carry out an active watch on Data Science subjects

Watch remains essential for the data scientist. Indeed, he must be aware of the latest analysis methods and tools inherent in his sector to guarantee its effectiveness. A fortiori, beyond the methods and tools, there may be new approaches in terms of data prioritization, for example.

Diversified profiles

Unfortunately, too few dedicated training courses exist to train data scientists, due to its recent nature. The profiles applying for offers related to this profession are therefore very diverse, often seeing themselves coming from related fields, such as:

  • The statistics.
  • Computing.
  • Actuarial science.

In other words, to find a suitable profile for a data scientist position, you have to understand the “close” sectors and accept the candidacy of professionals who come from them, since they can present all the required skills, despite the “absence”. diploma.

Varied and specialized technical skills

As a first step, the data scientist must present solid knowledge in statistics, omnipresent in the exercise of this profession. It must be able to manage data that is poorly, if at all, structured. In addition, strong expertise in machine learning and in software engineering is strongly recommended, in order to guarantee the employer the agility of the candidate in terms of tools and interfaces.

Secondly, from a more operational point of view, the data scientist must demonstrate expertise in terms of tools dedicated to analysis, both from a SAS or R language. Finally, he must be perfectly comfortable with programming languages:

  • Python.
  • Java.
  • Perl.
  • C / C ++.

Intellectual curiosity and communication skills

In terms of interpersonal skills, a person applying for a data scientist position must have the following qualities:

  • Curiosity and initiative necessary to detect the most interesting and exploitable data within a gigantic volume of data.
  • Creativity to use this data in a relevant way.
  • Ability to communicate the results in a comprehensible and clear manner (adapting a technical language to a “business” language).
  • Good listening to understand the needs of its interlocutors.
  • Counseling posture with decision-makers.

The profession of data scientist is one of the best paid professions in the world of big data, due to several factors:

  • Rare adapted profiles, because it is about “niche” knowledge.
  • A great deal of necessary expertise and exemplary interpersonal skills.
  • The lack of dedicated training, making candidates likely to work in other sectors.

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