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Career path

Machine Learning Engineer

Bootcamp (5,5 months)
or
Part-time (16 months)
Get a recognized diploma, support until you are hired and a flexible job that is in high demand.
Download program
OUR NEXT ENTRIES ARE:
January 07, 2025
February 04, 2025
March 04 , 2025
logo sorbonne
Certificate delivered by University La Sorbonne
Machine Learning Engineer video​

Training content​

icon 

Programming

  • Fundamentals of Python
  • NumPy
  • Pandas
data-viz 

Data Visualization

  • Matplotlib
  • Seaborn
  • Bokeh
illu-2 

Machine Learning

  • Classification
  • Regression
  • Clustering with Scikit-learn
illu-3 

Advanced Machine Learning

  • Time series
  • Text Mining
  • Dimension reduction
illu-4 

Data Engineering

  • Database
  • SQL
  • PySpark
illu-1 

Deep Learning

  • Neural networks
  • CNN & RNN with Keras
  • Tensorflow
  • PyTorch
illu-2 

Complex Systems and AI

  • Reinforcement Learning
  • Deep RL
illu-2 

Advanced Programming

  • Webscraping
  • Linux & Bash
  • Git & Github
  • Unit Tests
illu-2 

DataOps - Isolation

  • FastAPI
  • API security
  • Docker
  • Flask and Bootstraps
illu-2 

DataOps - Orchestrierung

  • Kubernetes
  • Airflow
illu-2 

ModelOps

  • MLflow
  • Data Acculturation

Throughout your Machine Learning Engineer training, you will carry out a 120-hour project.
The objective: apply your learning to a real problem and benefit from a first concrete achievement to add to your portfolio.

This course includes an AWS Cloud Pracitioner course leading to an official AWS certification.
Download program

A Machine Learning Engineer missions

The Machine Learning Engineer develops artificial intelligence (AI) systems using large data sets to research, develop and generate algorithms capable of learning and predicting. He/she masters the entire Machine Learning process, from the design of the algorithm to its deployment and production.

Study and analyze

Investigate and analyze relevant data related to the company’s production process, sales or customer data set.

To predict

Develop predictive models to anticipate an evolution or to determine an interesting target value for the company

Deployment

Put Machine and Deep Learning algorithms into production and deploy models on Cloud solutions

To implement

Exploit the results of data analysis and modeling to make them readable, usable and actionable by the other departments of the company.

Book an appointment
Download program

Discover Learn, our learning platform

A user-friendly, comprehensive interface for a tailor-made learning experience. An enhanced platform and premium coaching.

Discover the platform

Key figures of the training

91%

Success rate

95%

Completion rate

97%

Satisfaction rate

78,7%

Insertion rate

Our goal is to make our courses affordable and open to everyone - regardless of one's current situation. This means we do our best to offer as many financing options as possible.

If you live in France, you can benefit from several financing options:

  • CPF: If you have already worked in France, you may have accumulated a budget allocated for training, which allows you to finance your training via your CPF account
  • Personal financing: It is possible to spread out your payment in several instalments in order to finance your training.
  • Company financing: If you are an employee, you can have your training financed by your company.
  • Pôle Emploi: If you are a job seeker and registered with Pôle Emploi, it is possible to benefit from total or partial financing via Pôle Emploi.
  • Transitions Pro: Do you want to retrain while keeping your job? You can use the system via Transitions Pro.
  • Region: If you are registered with Pôle Emploi, you can also benefit from funding from your region! Several schemes exist that allow you to finance your training.

Don’t hesitate to make an appointment with one of our advisors to find the funding that best suits you!

If you are living in Germany you have multiple ways to finance your training courses depending on your professional situation.

Employees:

  • Funding from your employer: You can check with your employer to see if there is a possibility of having your training paid for (totally or partially paying for your training).
  • Payment by installments: If you are unable to pay the entire amount at once, you may be interested in our installment plan (pay the costs over a period of up to 12 months).

Your company may also be able to benefit from the Qualifizierungschancengesetz and get funding from the state.

Unemployed, job seekers, self-employed or students:

  • Bildungsgutschein: If you are looking for work, threatened by unemployment, self-employed or even a student, you have a good chance of receiving an education voucher (Bildungsgutschein). Contact your advisor at the employment agency or the job center and check whether there is a possibility of funding your training course.
  • Self-financing: If you have no chance of receiving the education voucher, you can pay the remaining amount by bank transfer, direct debit or credit car.
  • Payment by installments: If you are unable to pay the entire amount at once, you may be interested in our installment plan (pay the costs over a period of up to 12 months).

Get more information about the process and the next steps by downloading our Bildungsgutschein guide.

The DataScientest team will help you find the best funding for your personal circumstances.

Different types of financing can be applied depending on your current situation:

  • Fundae: Thanks to our close links with companies and our high employment rate, you can subsidise our courses with Fundae.
  • Pledg: Finance our courses in up to 12 months.
  • Quotanda: Finance the course with Quotanda interest-free (+12 months).
  • Student Finance: You pay nothing until you find a job.

For further information, please check this page and book an appointment with our team.

Book an appointment

Would you like to discover the job of a Machine Learning Engineer?

Data science jobs are constantly evolving. It is essential to define each of them in order to better understand companies’ current expectations and thus align training and hiring opportunities.

Among them is the Machine Learning Engineer, a profession in full expansion. Find all the information you need by downloading this complete job description: expected skills, tools & technologies, career prospects and salary.

Job description

What our alumni say about our DataScientest training courses!

Patricia Jan, Data Scientist and alumni of DataScientest, tells you today in a video about her experience of further training and how data plays a role in her everyday life!

🎉 Would you also like to get started with one of our courses? New courses start every month and good news: We’ve just launched the DevOps course to extend our Data Science trainings!

Download program
“I found the training very interesting. The content is very rich and accessible. The 75% autonomy format is particularly beneficial. By being mentored and "pushed" to pass certifications to reach specific milestones, it maintains a pace."
Adrien M.
Adrien M.Data Scientist @Siderlog Conseil
"The curriculum is very educational and covers all the notions necessary for the position of a data scientist: from learning python to data acquisition, notably via web scrapping to the most advanced skills in machine learning, deep learning, and reinforcement learning."
Cyrille C.
Cyrille C.Data Engineer @GoWizYou
“The courses available on the platform are very pedagogical, and you gradually gain competences on complex concepts. We have access to many resources to help us, a forum, a live chat and the possibility to make regular video calls with the supervisors”.
Emeline S.
Emeline S.Data Scientist @Green-Got
"I took the part-time course at DataScientest. The training was complete, perfectly surveyed, and completely professionalizing. I can only recommend the institute's training courses and I intend to follow others!"
Victor L.
Victor L.Data Scientist @Veltys
Previous
Next

You have questions ? We have the answers!

Accordion Content

The Machine Learning Engineer appeared with the evolution of needs and missions given to Data Science teams. Professional experts in the data world have faced a demand for technical knowledge related to model training collaboration. Companies are therefore facing growing needs for automation and deployment of predictive models on the cloud.

Organizations, companies, public sectors and associations increasingly need to provide their customers, partners or public with predictive models based on Machine Learning.

The objective of the Machine Learning Engineer is to take a prediction model based on Machine Learning, to condition it (thanks to APIs and containers), to test it (with unit tests) and to set up its deployment on a Kubernetes cluster.

The Machine Learning Engineer is a versatile expert who occupies a major place in Data Science teams.

So don’t hesitate any longer and join our expert course!

The Machine Learning Engineer shares common missions with the Data Scientist. Like him, he develops Machine Learning algorithms in order to solve classification and recommendation problems, but the Machine Learning Engineer can deploy these models, for example, on the Cloud.

A Machine Learning Engineer can evolve in many sectors. He will notably work on anomaly detection, fraud detection, search ranking, text/sentiment classification, spam detection and many other aspects of Machine Learning.

A Machine Learning Engineer is also responsible for guiding the use of technologies, data and Machine Learning.

The missions are thus diverse and varied. In particular, he applies software development practices and standards in order to develop robust and long-lasting solutions. To do this, he must maintain an active role in each part of the development life cycle of Machine Learning-based solutions. It is also necessary for him to guide non-technical teams in understanding best practices to guide the development of these solutions.

Here are some missions of the Machine Learning Engineer:

  • Put Machine and Deep Learning algorithms into production;
  • Master the techniques of manipulation and pre-processing of data;
  • Develop APIs;
  • Automate the training of predictive models and deployment on the Cloud (containerization).
Accordion Content

In order to participate in the Machine Learning Engineer course, you should have a good knowledge of mathematics or statistics, which can be demonstrated by a bachelor’s degree, for example.
These prerequisites exist because although the training is centered on data science, and not mathematics, these are necessary for a good understanding of the logical principles of the concepts covered.Furthermore, programming is essential for the development and production of any machine learning project. For this, a certain level of programming knowledge is an advantage. Since the terminology, documentation and online resources are in English, you should speak at least a B1 level English.

After your registration on the site, we contact you for the first time for a presentation of what DataScientest is, what we can offer you but also your background and your wishes. The idea is to align your expectations from that moment with our training courses.

You can of course also make an appointment directly by clicking here ! 

Then we redirect you to a technical positioning test that we use to know what bases you are starting with . These are essentially mathematical questions dealing mainly with basic notions (L1/L2 level) in probability, statistics, analysis and algebra.

Once this test has been passed, a member of the admissions team will contact you to discuss your results and validate your professional project, your motivations, and finally the relevance of your educational project.

Once your project is confirmed, you go to the registration phase with our teams who will take care of initiating your Machine Learning Engineer training and setting it up with you in all its aspects.

At the end of your training you will know:

  • Prepare data, set up a data analysis strategy and master Python programming;
  • Train predictive Machine Learning models and automate the updating of these models;
  • Manipulate neural networks and implement Computer Vision or Natural Language Processing algorithms;
  • Exploit artificial intelligence models in a production context;
  • Implement a classic Machine Learning algorithm and use it on text data
  • Deploy models on cloud solutions;
  • Build Data Visualizations allowing the valuation of results.

The course is based on sequences themselves divided into modules that allow you to master the skills deemed necessary for the profession of Machine Learning Engineer. 

Thanks to our studies with our DataBoss, Alumni etc. communities, our data science experts have been able to build a course that precisely meets the skills sought by recruiters.

Thus, throughout the training, you will master the following tools: Python, Git and Github, Flask, FastAPI, Docker, Kubernetes, Airflow…

For a total hourly volume of 550 hours of training, 85% of your training takes place on a personalized coaching platform while the remaining 15% is in the form of a masterclass where an experienced teacher leads a course and answers all your questions.

Beyond the platform and the masterclasses, you will work on a common thread project which will confirm the skills acquired and thus allow you to be directly operational.

The Machine Learning Engineer training is available in the “continuing education” format which requires an involvement of 10 hours per week for 13 months. Make an appointment to find out more

Obviously ! And who better to provide support than our teachers , who also designed the program. They are available and attentive to any questions, whether theoretical or practical. 

They also follow the progress of learners closely so that everyone is neither neglected nor demotivated. Each disconnection of a certain duration will be communicated to your cohort manager who will then hear from you so as not to leave you in difficulty!

Finally, our papers, exams and defenses are also corrected by hand by our panel of qualified teachers: everything is done so that everyone can progress effectively at their own pace . At DataScientest we are convinced that only personalized monitoring ensures quality learning!

Throughout your training, and as your skills are developed, you will lead a project for an artificial intelligence solution.

This project may come from our catalog, composed of various subjects, with technical business issues and using rich and complex data. You can also propose a personal project, as long as the data is accessible and our teaching team validates it.

It is an extremely effective way to move from theory to practice and to ensure that you apply the themes covered in class.

It is also a project highly appreciated by companies because it ensures the quality of the training and the knowledge acquired at the end of the Machine Learning Engineer training since the use of soft-skills is also very present.

  • Ability to transmit information
  • Know how to present and popularize your work
  • Know how to highlight data with interactive tools (Dashboard, Streamlit, etc.)

In short, it is a project that will require a real investment: at least a third of your time spent on training will be on this project .

The project is supervised by a DataScientest mentor who will regularly discuss with you to ensure your progress and to guide you.

According to the data managers of the largest CAC 40 groups, knowing how to communicate both orally and in writing is more important than mastering the core business of the company for a Data Scientist and therefore for a Machine Learning Engineer. .

We have therefore taken this into account in our curriculum which also emphasizes Soft Skills with:

  • The written and oral defenses of the project, which allow these skills to be developed.
  • Masterclasses dedicated to project management and the interpretation of results.
  • Masterclasses on best practices in “data visualization” and on dedicated tools.

 

You will also have the opportunity to participate in CV workshops and career coaching via careers managers and the DataScientest HR team.

French leader in Data Science training, DataScientest enjoys a great reputation among companies who entrust it with the data science training of their teams. This confidence has forged the recognition of his diplomas.
Accordion Content

As for the Data Scientist, Data Analyst or Data Engineer, the salary to which a Machine Learning Engineer can claim varies according to his experience, the company that hires him and the city of exercise of his professional activity.

On average, a Junior Machine Learning Engineer can earn between €35,000 and €40,000/year . The salary of an expert can go up to 60,000€/year . The average salary in France is €40,000 per year, while it can exceed one hundred thousand euros in the United States!

The demand for work and therefore the job offer in AI and in particular in Machine Learning Engineering is booming. The Machine Learning labor market is even currently in short supply. Companies are becoming more and more aware of the added value of Machine Learning to take full and more effective advantage of their data and are struggling to find the right profiles. This opens the doors even more to candidates and puts upward pressure on salaries!

Today there are hardly any sectors that do not compete for talent. The applications of Machine Learning affect the fields of education as well as health, industry, IT, etc. Moreover, they are as varied as the data itself: image and speech recognition, customer knowledge, risk management and fraud prevention.

Accordion Content

Beta tests are available for our alumni in order to gain data knowledge even after the end of the training. 

In parallel, newsletters drawn up by our data scientists are regularly sent and are a reliable source of specialized information in data science. 

Finally, the DataScientest community continues to grow, and with it all of its alumni. To keep in touch and allow former students to communicate with each other, DataScientest has set up a  group of alumni on LinkedIn  who share and discuss various themes around Data Science.

The  DatAlumni community  is a  LinkedIn community  that brings together DataScientest alumni. On this page, questions, tips and technology news are shared for everyone’s benefit. 

In addition to this, DataScientest will launch in the coming weeks a trombinoscope which will put alumni in contact, this one will include the company and the position of each one.

Initially, DataScientest supported the data transition of companies . This has made it possible to create strong links between the major groups which have ensured the growth of our structure . 

Subsequently, they are the ones who motivated the launch of our offer to individuals in order to compensate for the lack of competent profiles. This need for good profiles is reflected in the survey we conducted among 30 CAC 40 groups . Even if they had tight budget constraints, only 4% believe they would downsize their data scientist workforce; by comparison, 28% would still seek to increase their number by more than 20%

On the strength of our past with large companies, we then signed partnerships linked to the hiring of our alumni . All the partner companies undertake to include all our students at the end of their training in their recruitment process : this, coupled with help with CVs and interviews, means that you will be in pole position to land the job of your dreams!

With our experience with large companies, we regularly organize recruitment fairs with our partner companies, addressed to all our students and alumni.

On the first day of your entry into training, a platform dedicated to career services containing all the workshops essential to your job search will be presented to you.

You can access it continuously, even after the end of your training.

Mathilde and Morgane, our career managers are entirely dedicated to you throughout your training. It is possible to make an appointment individually with one of them in order to accompany you and answer any questions you may have about your career plan.

In addition to this, career workshops are organized every month:

  • A workshop to help you write a good CV and data-oriented Linkedin
  • A workshop to help you strategize your job search with different topics on presentation, career change, salary negotiation and technical test training.

 

In addition to these subjects, there are other workshops to be defined according to individual needs. On the other hand, concrete actions are implemented to support you in your job search: recruitment fair organized by DataScientest with its partner companies, organization of Webinars with data experts, communication actions to boost your visibility (CV competition, DataDays, project articles published on the blog and external reference media). 

To find out about all of DataScientest’s career support actions, click on this link .

The job
Accordion Content

The Machine Learning Engineer appeared with the evolution of needs and missions given to Data Science teams. Professional experts in the data world have faced a demand for technical knowledge related to model training collaboration. Companies are therefore facing growing needs for automation and deployment of predictive models on the cloud.

Organizations, companies, public sectors and associations increasingly need to provide their customers, partners or public with predictive models based on Machine Learning.

The objective of the Machine Learning Engineer is to take a prediction model based on Machine Learning, to condition it (thanks to APIs and containers), to test it (with unit tests) and to set up its deployment on a Kubernetes cluster.

The Machine Learning Engineer is a versatile expert who occupies a major place in Data Science teams.

So don’t hesitate any longer and join our expert course!

The Machine Learning Engineer shares common missions with the Data Scientist. Like him, he develops Machine Learning algorithms in order to solve classification and recommendation problems, but the Machine Learning Engineer can deploy these models, for example, on the Cloud.

A Machine Learning Engineer can evolve in many sectors. He will notably work on anomaly detection, fraud detection, search ranking, text/sentiment classification, spam detection and many other aspects of Machine Learning.

A Machine Learning Engineer is also responsible for guiding the use of technologies, data and Machine Learning.

The missions are thus diverse and varied. In particular, he applies software development practices and standards in order to develop robust and long-lasting solutions. To do this, he must maintain an active role in each part of the development life cycle of Machine Learning-based solutions. It is also necessary for him to guide non-technical teams in understanding best practices to guide the development of these solutions.

Here are some missions of the Machine Learning Engineer:

  • Put Machine and Deep Learning algorithms into production;
  • Master the techniques of manipulation and pre-processing of data;
  • Develop APIs;
  • Automate the training of predictive models and deployment on the Cloud (containerization).
Training
Accordion Content

In order to participate in the Machine Learning Engineer course, you should have a good knowledge of mathematics or statistics, which can be demonstrated by a bachelor’s degree, for example.
These prerequisites exist because although the training is centered on data science, and not mathematics, these are necessary for a good understanding of the logical principles of the concepts covered.Furthermore, programming is essential for the development and production of any machine learning project. For this, a certain level of programming knowledge is an advantage. Since the terminology, documentation and online resources are in English, you should speak at least a B1 level English.

After your registration on the site, we contact you for the first time for a presentation of what DataScientest is, what we can offer you but also your background and your wishes. The idea is to align your expectations from that moment with our training courses.

You can of course also make an appointment directly by clicking here ! 

Then we redirect you to a technical positioning test that we use to know what bases you are starting with . These are essentially mathematical questions dealing mainly with basic notions (L1/L2 level) in probability, statistics, analysis and algebra.

Once this test has been passed, a member of the admissions team will contact you to discuss your results and validate your professional project, your motivations, and finally the relevance of your educational project.

Once your project is confirmed, you go to the registration phase with our teams who will take care of initiating your Machine Learning Engineer training and setting it up with you in all its aspects.

At the end of your training you will know:

  • Prepare data, set up a data analysis strategy and master Python programming;
  • Train predictive Machine Learning models and automate the updating of these models;
  • Manipulate neural networks and implement Computer Vision or Natural Language Processing algorithms;
  • Exploit artificial intelligence models in a production context;
  • Implement a classic Machine Learning algorithm and use it on text data
  • Deploy models on cloud solutions;
  • Build Data Visualizations allowing the valuation of results.
The curriculum

The course is based on sequences themselves divided into modules that allow you to master the skills deemed necessary for the profession of Machine Learning Engineer. 

Thanks to our studies with our DataBoss, Alumni etc. communities, our data science experts have been able to build a course that precisely meets the skills sought by recruiters.

Thus, throughout the training, you will master the following tools: Python, Git and Github, Flask, FastAPI, Docker, Kubernetes, Airflow…

For a total hourly volume of 550 hours of training, 85% of your training takes place on a personalized coaching platform while the remaining 15% is in the form of a masterclass where an experienced teacher leads a course and answers all your questions.

Beyond the platform and the masterclasses, you will work on a common thread project which will confirm the skills acquired and thus allow you to be directly operational.

The Machine Learning Engineer training is available in the “continuing education” format which requires an involvement of 10 hours per week for 13 months. Make an appointment to find out more

Obviously ! And who better to provide support than our teachers , who also designed the program. They are available and attentive to any questions, whether theoretical or practical. 

They also follow the progress of learners closely so that everyone is neither neglected nor demotivated. Each disconnection of a certain duration will be communicated to your cohort manager who will then hear from you so as not to leave you in difficulty!

Finally, our papers, exams and defenses are also corrected by hand by our panel of qualified teachers: everything is done so that everyone can progress effectively at their own pace . At DataScientest we are convinced that only personalized monitoring ensures quality learning!

Throughout your training, and as your skills are developed, you will lead a project for an artificial intelligence solution.

This project may come from our catalog, composed of various subjects, with technical business issues and using rich and complex data. You can also propose a personal project, as long as the data is accessible and our teaching team validates it.

It is an extremely effective way to move from theory to practice and to ensure that you apply the themes covered in class.

It is also a project highly appreciated by companies because it ensures the quality of the training and the knowledge acquired at the end of the Machine Learning Engineer training since the use of soft-skills is also very present.

  • Ability to transmit information
  • Know how to present and popularize your work
  • Know how to highlight data with interactive tools (Dashboard, Streamlit, etc.)

In short, it is a project that will require a real investment: at least a third of your time spent on training will be on this project .

The project is supervised by a DataScientest mentor who will regularly discuss with you to ensure your progress and to guide you.

According to the data managers of the largest CAC 40 groups, knowing how to communicate both orally and in writing is more important than mastering the core business of the company for a Data Scientist and therefore for a Machine Learning Engineer. .

We have therefore taken this into account in our curriculum which also emphasizes Soft Skills with:

  • The written and oral defenses of the project, which allow these skills to be developed.
  • Masterclasses dedicated to project management and the interpretation of results.
  • Masterclasses on best practices in “data visualization” and on dedicated tools.

 

You will also have the opportunity to participate in CV workshops and career coaching via careers managers and the DataScientest HR team.

French leader in Data Science training, DataScientest enjoys a great reputation among companies who entrust it with the data science training of their teams. This confidence has forged the recognition of his diplomas.
The career
Accordion Content

As for the Data Scientist, Data Analyst or Data Engineer, the salary to which a Machine Learning Engineer can claim varies according to his experience, the company that hires him and the city of exercise of his professional activity.

On average, a Junior Machine Learning Engineer can earn between €35,000 and €40,000/year . The salary of an expert can go up to 60,000€/year . The average salary in France is €40,000 per year, while it can exceed one hundred thousand euros in the United States!

The demand for work and therefore the job offer in AI and in particular in Machine Learning Engineering is booming. The Machine Learning labor market is even currently in short supply. Companies are becoming more and more aware of the added value of Machine Learning to take full and more effective advantage of their data and are struggling to find the right profiles. This opens the doors even more to candidates and puts upward pressure on salaries!

Today there are hardly any sectors that do not compete for talent. The applications of Machine Learning affect the fields of education as well as health, industry, IT, etc. Moreover, they are as varied as the data itself: image and speech recognition, customer knowledge, risk management and fraud prevention.

Our services
Accordion Content

Beta tests are available for our alumni in order to gain data knowledge even after the end of the training. 

In parallel, newsletters drawn up by our data scientists are regularly sent and are a reliable source of specialized information in data science. 

Finally, the DataScientest community continues to grow, and with it all of its alumni. To keep in touch and allow former students to communicate with each other, DataScientest has set up a  group of alumni on LinkedIn  who share and discuss various themes around Data Science.

The  DatAlumni community  is a  LinkedIn community  that brings together DataScientest alumni. On this page, questions, tips and technology news are shared for everyone’s benefit. 

In addition to this, DataScientest will launch in the coming weeks a trombinoscope which will put alumni in contact, this one will include the company and the position of each one.

Initially, DataScientest supported the data transition of companies . This has made it possible to create strong links between the major groups which have ensured the growth of our structure . 

Subsequently, they are the ones who motivated the launch of our offer to individuals in order to compensate for the lack of competent profiles. This need for good profiles is reflected in the survey we conducted among 30 CAC 40 groups . Even if they had tight budget constraints, only 4% believe they would downsize their data scientist workforce; by comparison, 28% would still seek to increase their number by more than 20%

On the strength of our past with large companies, we then signed partnerships linked to the hiring of our alumni . All the partner companies undertake to include all our students at the end of their training in their recruitment process : this, coupled with help with CVs and interviews, means that you will be in pole position to land the job of your dreams!

With our experience with large companies, we regularly organize recruitment fairs with our partner companies, addressed to all our students and alumni.

On the first day of your entry into training, a platform dedicated to career services containing all the workshops essential to your job search will be presented to you.

You can access it continuously, even after the end of your training.

Mathilde and Morgane, our career managers are entirely dedicated to you throughout your training. It is possible to make an appointment individually with one of them in order to accompany you and answer any questions you may have about your career plan.

In addition to this, career workshops are organized every month:

  • A workshop to help you write a good CV and data-oriented Linkedin
  • A workshop to help you strategize your job search with different topics on presentation, career change, salary negotiation and technical test training.

 

In addition to these subjects, there are other workshops to be defined according to individual needs. On the other hand, concrete actions are implemented to support you in your job search: recruitment fair organized by DataScientest with its partner companies, organization of Webinars with data experts, communication actions to boost your visibility (CV competition, DataDays, project articles published on the blog and external reference media). 

To find out about all of DataScientest’s career support actions, click on this link .

Are you interested?

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