Data Scientist job: salary, training, missions and skills

Job Data Scientist, Salary, training, missions and skills

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Considered as the "sexiest" job of the 21st century, Data Scientist is a position that is more and more sought after because data has become a strong growth lever for companies. For several years now, the Data market has been growing, even during the covid period. Do you want to become a Data Scientist in 2023? Let's take a look at this job, the salary, the evolution perspectives and the Data Science trainings you can follow to become a Data Scientist.

What is a Data Scientist?

The Data Scientist is the professional who will give value to the data of a company. If you have no idea what data is, just imagine an Excel sheet. Each cell of this sheet is composed of a data (some call it a statistic) and you can do a lot of things with it!

Of course, in a company, data is not organised in simple Excel sheets, you will have databases designed to store large volumes of data (Big Data). These databases are of the SQL or NoSQL type depending on the needs. For this, there are many differences between the preconceived notions of the data scientist's job and the field. Here we explain everything.

The role of the Data Scientist is therefore to use the available data to carry out descriptive statistical analyses or to create Machine Learning algorithms that will allow complex tasks to be automated or processes already in place to be optimised.

The Data Scientist's job
The Data Scientist's job

What are the missions of the Data Scientist?

The job of a Data Scientist is very cross-functional, whether you want to Become a Freelance Data Scientist or not. As a result, the tasks are varied. Here are a few examples of what you can do:

  • Develop a Machine Learning algorithm to optimize a process or automate a task (e.g. determine an optimal discount percentage to boost sales during sales);
  • Collect data to feed databases or to build an algorithm;
  • Analyze Big Data with tools like Spark ;
  • Clean up the data to make it usable. For example, we can detect outliers or replace missing values;
  • Present analysis results by creating Dashboards for example.

At what stage of a Data project do we find the Data Scientist?

If we were to summarise data projects, we would describe them as a pipeline with several stages. The following steps are found in a Data project:

  1. Data collection: You will need to find the data you need for your analyses. It may be in databases, you will have to scrape it from the web or you can get it from your CRM!
  2. Exploration: Once you have the data, you will have to clean and prepare it. To do this, we often carry out an exploratory phase called EDA for Exploratory Data Analysis. The objective is to detect outliers or missing values, for example.
  3. Exploitation: Now that everything is ready, you can use the data for the objective that has been set. For example, in Data Science, you will develop a Machine Learning algorithm.
  4. Production: Once you have successfully developed your AI algorithm or solution, you will need to put it into production at enterprise scale. This involves different technologies and is a separate step.

As a Data Scientist, you will mainly be found in the first stages, from collection to exploitation. You won't find him or her as much in the production phase, even though more and more Data Scientists are working in this area because it's a good way to stand out from the crowd of recruiters who are looking for the rare gem.

What are the 6 skills to have as a Data Scientist?

If you want to become a Data Scientist, you will need to acquire skills in statistics and programming. Don't hesitate to read our article on the Top 6 skills to become a Data Scientist which will give you a priority order on the skills to focus on.

Overall, these are the main pillars:

  • Programming: This is a key skill to be a data scientist. We advise you to focus on Python as the language that is by far the most popular and easiest to learn.
  • Statistics: There is a mathematical dimension to Data. Although you don't have to be a trained statistician, it is important that you have a solid grounding in statistics (averages, medians, confidence intervals), linear algebra (managing matrices) and functions (derivatives etc.)
  • Machine Learning & Deep Learning: Although this falls into the world of statistics, it will still be a subject you will be working on a lot and so was important to mention. You will be designing Machine Learning and Deep Learning algorithms, very often in Python for your business needs.
  • Data Visualisation: When you are going to carry out analyses, you will very often have to present them to neophytes and what better way than with a beautiful graph to support your point. This is the principle of Data Visualisation!
  • SQL database management : When collecting data, you will need to use SQL. This is a highly demanded skill among data scientists and data analysts.
  • Big Data: More and more often, companies have so-called Big Data. The infrastructure is slightly different when you have this type of data and that is why you have to learn how to use it. Very often the Spark framework is used for this.

What is the average salary of a Data Scientist?

Depending on the type of company and the sector, salaries vary, but here is a grid:

Average salary Data Scientist
Source - Glassdoor

Which training courses should I take to become a Data Scientist?

The Tech professions are open to a wide range of profiles, You don't need to be an engineer to become a Data Scientist. You might think that only engineers or statisticians could aspire to this profession, but this is not the case. However, you will need to train for it and for that you can choose from three options:

  • Online training: is great for getting started and training at your own pace but difficult to gain credibility in the market with only online training
  • Masters in Data: where you can have more comprehensive training and a state-recognised degree. However, you will need to invest between €10,000 and €20,000 and at least one year of your time.
  • Intensive Bootcamp training: This is the right compromise between the flexibility of an online course and the theoretical depth of a master. Data bootcamps have become a real alternative to traditional training because they are very practical and will teach you skills that are directly applicable in companies. Do not hesitate to take a look at our intensive Data Science training courses, ranked as the best Data bootcamp course in France!

Depending on your background, your initial level and your aspirations, you will opt for one type of training rather than another. We have written a whole article to help you choose the best Data training for you. You will find all our advice for becoming a Data Scientist. Don't hesitate to take a look.

What are the prospects for the development of this profession?

When you start your career in Data, you will find that many opportunities will open up for you. Once you have gained experience as a Data Scientist, you can aspire to higher responsibility positions of which here is an overview:

  • Lead Data Scientist
  • Chief Data Officer

You can also make a more horizontal progression with trades such as :

  • Machine Learning Engineer
  • Data Engineer

You will make your own path. The important thing to remember is that the field of data offers so many possibilities that you won't have enough of a career to discover all of them.

Our 3 tips for becoming a Data Scientist

If you want to get started in Data, here are some tips for applying for Data Scientist positions:

  • Highlightyour technical skills but also your business skills: A data scientist does not stand alone, knowing the industry in which they work is a real plus when it comes to analysing data.
  • Do projects: if you don't have professional experience, that's okay! You can get some by doing open source projects on Kaggle for example. Don't hesitate to do as many as possible to put them in your portfolio.

If you want to know more, don't hesitate to read our article on the 4 strategies to become a Data Scientist.

Frequently asked questions about the Data Scientist job

How to become a Data Scientist without a degree?

To become a Data Scientist without a degree, you first need to learn programming skills, mastering popular data programming languages such as Python, SQL or R. Statistics and data analysis are also no secret to you. Gain experience by practicing on real projects with the goal of training, and a complete professional portfolio for future interviews.

Why hire a Data Scientist?

The Tech field is booming, the data sectors do not stop recruiting talents in order to compensate the lack of professionals. Hiring a Data Scientist is to secure the proper functioning of its market. A company that wants to increase its business in 2023, must hire specialists.

Why choose data science?

Data science is an exciting field because it is interdisciplinary, and allows you to discover hidden insights in the data, enabling you to make decisions to solve complex problems. The demand for data science professionals is constantly growing, which guarantees a high career and salary potential.

Antoine Krajnc
Written by
Antoine Krajnc