What is a Data Scientist?

Once data has been collected, transformed, and formatted by the Data Engineer, the Data Scientist extracts value from it by designing algorithms that will then be used to study behaviors and build predictive models supporting the strategic decisions of business teams and leadership.

Their goal is to process a huge volume of data to deliver relevant analysis grounded in data.

Job profile updated 04/14/2026.

Why do companies need a Data Scientist?

Data speaks for itself and proves the person who uses it right. Relying on data means making sure to base a decision on a massive sample that no group of employees, survey, or study could ever cover.

The more data they collect or capture, the more they will need data scientists to process and leverage it — whether for internal decision-making or to improve their product by making it more performant through prediction.

In essence, the data scientist is the new mathematician/statistician in the era of tech.

Team collaboration

In a small tech team, they may carry the responsibility of an entire data function, combining the Data Engineer and Data Analyst roles. They will own the entire data value chain without being able to go deep into any single topic.

This requires horizontal knowledge of data issues without the ability to develop vertical expertise.

In a large tech team, they work under the responsibility of a Data Manager (Head of Data, CDO, Lead Data Manager), in collaboration with the Data Engineer who provides them with a volume of so-called "clean" data ready to be used.

The Data Analyst may work on the same decision-making problems but with a different output. The Data Analyst will develop visual tools (dashboards) and reporting, while the Data Scientist will build predictive models.

Outside the data function, the Data Scientist can also be attached to the leadership of a business team to work on their issues in direct contact with those teams.

What kinds of problems does the Data Scientist tackle?

They will work on:

• Decision-making: studying the probability of success in a new market by comparing risks and benefits (banks and insurance), determining the type of profile that stays and thrives in a company through predictive models to optimize hiring (psychological tests), studying the best layout of a wind farm to obtain the best possible yield.
• Improving the product/service: analyzing customer behavior to predict their next purchase, or studying user behavior to evolve an unused/poorly used feature.
• Data science as a technology: facial recognition on a smartphone or audio recognition to identify a song, processing and correcting images/photos to automate redundant, time-consuming, and tedious tasks, optimizing routes based on means of transport, or recommending content based on previous choices (news articles, songs, and films on streaming platforms).

Technologies & platforms used

• DB language: SQL, NoSQL
• DB management: MongoDB, Cassandra, Hbase
• Data management & visualization: Power BI, Elastic Suite, Tableau

For programming, here are the languages and frameworks used:

• Languages: R, Python, Java + Scala, C++
• Big Data frameworks: Spark, Hadoop, Hive

They use libraries like TensorFlow (Deep Learning) with Python.

They have cloud skills: Microsoft Azure, AWS, GCP.

They also know Web Analytics very well: Omniture, Google Analytics.

What training is needed to become a Data Scientist?

To qualify for this role, you need training in:

• Engineering school
• University curriculum specialized in computer science and statistics, Big Data Analytics, management, statistics.

A solid understanding and command of mathematics applied to statistics and probability is essential.

Specialized bootcamps also offer a path into the role in a few months. Jedha teaches data science and AI engineering through a data scientist training program of 450 hours covering Python, machine learning, deep learning, and generative AI, with a 93% placement rate one year after the training.

What is a Data Scientist's salary?

A Data Scientist's salary starts at €35K/year and can go up to €100K gross/year. They have comfortable salaries with strong progression over the years:

Junior Data Scientist: €35K to €50K

Mid-level Data Scientist: €45K to €65K

Senior Data Scientist: €60K to €100K+.

How can a Data Scientist's career progress?

The Data Scientist can progress toward management or expertise roles:

• Management: Lead Data Scientist, Head of Data, Chief Data Officer
• Expertise: Big Data expert, Senior Data Science / Big Data consultant.

Are you a technical profile looking for new career opportunities? Don't miss our latest job openings.

Looking to hire a Data Scientist for your company? We can help. Bluecoders specializes in recruiting tech profiles. Get in touch.

FAQ about Data Scientists

What is a Data Scientist?

A Data Scientist is a data expert who analyzes large volumes of information to build models for analysis and prediction. Their goal is to help the company better understand, anticipate, and decide.

What does a Data Scientist do day to day?

Day to day, they collect and analyze data, build algorithms, test predictive models, and work on concrete cases such as optimizing a product, anticipating behavior, or supporting decision-making.

What does a Data Scientist do for a company?

They turn data into useful decisions. A company calls on them to better manage activity, improve products, automate certain analyses, or predict trends.

What's the difference between a Data Scientist and a Data Analyst?

The Data Analyst mostly uses data to produce analyses, reports, and dashboards. The Data Scientist goes further with predictive models, algorithms, and more advanced approaches in statistics and machine learning.

What's the difference between a Data Scientist and a Data Engineer?

The Data Engineer builds and maintains the data infrastructure. The Data Scientist then uses that data to analyze it, build models, and address business problems.

What kinds of responsibilities can a Data Scientist have?

Their responsibilities can include data analysis, building predictive models, improving a product, content recommendation, image recognition, journey optimization, or supporting strategic decision-making.

What skills do you need to become a Data Scientist?

You need a solid foundation in statistics, probability, applied mathematics, programming, and data manipulation. You also need to know how to connect analyses to concrete business issues.

What languages do you need to master to be a Data Scientist?

Python is the most expected language. R, Java, Scala, and C++ can also be useful depending on the environment. SQL remains essential for querying databases.

What tools does a Data Scientist use?

They can use data visualization tools, SQL and NoSQL databases, machine learning frameworks, big data technologies, and cloud environments such as AWS, Azure, or GCP.

Do you need to be strong at math to become a Data Scientist?

Yes. Mathematics, statistics, and probability are at the heart of the role. Without a solid foundation on these subjects, it becomes difficult to build reliable models.

What training is needed to become a Data Scientist?

We primarily recommend an engineering school or a university curriculum specialized in computer science, statistics, data science, or big data. These are the most coherent paths to access the role.

Can you become a Data Scientist without a degree?

It's possible in some cases, but more difficult. The role requires a high technical level, so the most credible profiles remain those with solid training in computer science, engineering, or statistics.

Can you become a Data Scientist through a career change?

Yes, but career change requires a real level of skill development. To be credible, you need to acquire solid foundations in programming, statistics, modeling, and data understanding.

How much does a Data Scientist earn?

Salary evolves significantly with experience. A junior profile typically starts around €35K to €50K gross per year, while a senior profile can reach €60K to over €100K gross per year.

In which sectors does a Data Scientist work?

They can work in many sectors: banking, insurance, energy, mobility, digital products, platforms, recruitment, or any company that leverages a lot of data.

Does a Data Scientist work alone?

No. They often work with Data Engineers, Data Analysts, data managers, and business teams. The role is rarely isolated.

Does the Data Scientist role change depending on the company?

Yes. In a small structure, they can have a very broad role. In a more mature company, they focus more on modeling, experimentation, or specialized topics.

Why hire a Data Scientist?

We recommend this hire when a company wants to go beyond simple reporting and use its data to predict, optimize, automate, or better orient its decisions.

What career progression after Data Scientist?

After a few years of experience, a Data Scientist can move to roles like Lead Data Scientist, Head of Data, Chief Data Officer, or senior data expert.

Does the Data Scientist only do machine learning?

No. Machine learning is part of the role, but the position also includes data analysis, business understanding, statistical modeling, and decision support.

Is the Data Scientist role in demand?

Yes, because companies increasingly need profiles capable of leveraging data in advanced ways. It's a strategic role as soon as there's a real challenge in extracting value from data.