Career Development

The differences between Data Analyst, Data Scientist and ML Engineer

Jobs around Data are very common positions in companies and startups/scale-ups. The challenges of recovery, processing, analysis and construction of models around data and Big Data have enabled the development of numerous positions: Data Analyst, Data Scientist, Machine Learning Engineer, Data Engineer, etc.

This article will help you see more clearly among all these Tech professions that we find in many startups in France

The differences between the profession of Data Analyst and Data Scientist

The role of Data Analyst is of make the data speak. Thus, its main objective is to carry out analyses, dashboards and reporting for all of the company’s services. The work of the Data Analyst thus feeds the management of company performance and allows you to make the best possible business decisions. Its role is therefore to:

  • Collect and tprocess company data
  • Carry out the analyzes requested by Business and build the associated dashboards (Dashboards)

The Data Analyst is therefore an expert in databases (BDD), querying (SQL language), Data Visualization tools (Power BI, Tableau, Qlik)

The role of Data Scientist is of create Machine Learning models. It can rely on different types of data:

  • Figured or quantitative data
  • Texts, we then speak of NLP (Natural Processing Language)
  • Images

From this data, the Data Scientist builds and develops predictive algorithms which will be trained on increasingly large datasets and allow decisions to be made autonomously. We then speak of Machine Learning models, where an algorithm developed by a Data Scientist “learns”. These models make it possible to construct Artificial Intelligence or AI which has many applications. We can cite several cases of applications of these models:

  • Creation of sales prediction models
  • Automatic text generation based on input (ChatGPT is the most recent and best known example)
  • Image recognition in the medical environment to help radiologists in their diagnosis

In addition to being a Data expert, the Data Scientist has the skills to carry out these developments, generally using the Python language.

In summary, the main difference between these two professions is that the Data Analyst analyzes existing data, whereas the Data Scientist uses it in order to create Machine Learning models.

Looking for profiles in Data? Visit our dedicated page: Data recruitment firm

What about Data Engineer VS Data Analyst / Data Scientist?

The role of Data Engineer is to build the models and data pipelines which will then feed the Data Analysts and Data Scientists in carrying out their analyses, dashboards and models.

The main missions of a Data Engineer are therefore:

  • Managing a Cloud infrastructure (GCP, Azure, AWS)
  • Data project management: Implementation of data pipelines, management of a Data Lake / Data Warehouse…

In certain aspects, the skills of a Data Engineer are similar to those of a Backend Developer, with a stronger dimension around data subjects.

The differences between the profession of Data Scientist and ML Engineer

👉 For more information on this subject, find our dedicated article Machine Learning Engineer vs. Data Scientist

When we push even further on the subjects of Data Science, we arrive at an additional profession which is that of Machine Learning Engineer or ML Engineer. Disparities exist in these roles depending on the company.

Generally, the role of the Data Scientist is to develop algorithms to solve a problem. These are generally POC (Proof of Concept) which allow you to prove the interest of a Data Science solution for a given problema POC being a sort of prototype of a Machine Learning model.

The ML Engineer will aim toindustrialize the algorithm and allow the model to be scaled up on a larger dataset and over a longer period. It thus deals with the subjects of model deployment, re-training, etc. In certain aspects, the ML Engineer will implement Data Engineering skills to carry out his missions.

Some profiles combine a Data Scientist and ML Engineer role, while others are more specialized.

What about Deep Learning versus Machine Learning?

Deep Learning is a term that we often encounter when we talk about Data Science. It is a Machine Learning technique which relies on the creation of neural networks and allows the creation of even more complex algorithms. Deep Learning makes it possible to achieve levels of performance that are not achievable with traditional Machine Learning models.

Looking for profiles in Artificial Intelligence? Visit our page dedicated to recruitment in AI: AI recruitment firm

For further

Find all the Data Analyst job offers and the Data Scientist job offers and ML Engineer in startup on the Licorne Society recruitment platform.

For more information on the associated career paths, the training necessary for these professions, as well as on salary levels in startups, find all of the startup job descriptionsas well as our study on startup salaries

To go further on Tech professions, find our article on this subject on our blog which lists all of the Tech and Product professions in startups and scale-up, including the role of Head of Data.

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