소시에테 제네랄에서 데이터 사이언티스트를 채용합니다. 머신러닝 및 딥러닝 알고리즘을 활용하여 복잡한 데이터를 비즈니스 의사결정 도구로 변환하는 역할을 수행합니다. Python, R, Java 활용 능력과 통계적 지식이 필수이며, 금융 도메인에서 전략적 AI 솔루션을 설계하고 모델을 배포하는 경험을 쌓을 수 있습니다.
Data Scientist
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Transform raw data into decision-making tools
Join the Societe Generale community of over 200 data scientists – a number that’s likely to grow to meet the needs of the bank’s business areas. You will process considerable and rich sets of data to improve client service and operational efficiency, create new services and products and to control risks.
An in-house data lake of several data petabytes and efficient CPU/GPU computing architecture
Responsible and ethical use of data
Diverse, compelling projects
In-house communities of experts (NLP, Auto-ML, etc)
Meet Francesco, Data Scientist
Hello, my name is Francesco and I am a Senior Data Scientist at Societe Generale. I joined the Group in 2020 after my studies and several professional experiences in the United States. And after that, I came back to France and I joined the Innovation department of Societe Generale because I wanted to work on strategic subjects.
My job is to develop artificial intelligence solutions for the various banking professions. It is a job at the intersection of three disciplines: mathematics for the design of algorithms, software engineering which allows translating these algorithms into code and deploying projects within the bank's infrastructure and business expertise that is necessary to create relevant solutions, but also to interact with non-technical audiences and explain technical content to them.
If I had to name one key skill for this job, I would say it's the ability to structure a project and to put yourself into the user's shoes. So, you need to understand these issues, identify what they could need and conceive a relevant solution.
And more recently, I have been interested in GenAI questions.
We have many challenges ahead of us. The first one is to design solutions that are sufficiently specific to meet the needs of each job, but also sufficiently generic to cover all the Group's needs. And all while keeping the balance between added value and environmental impact. A second challenge is to provide solutions with as little bias, toxicity or hallucinations as possible that is to say, false answers that are presented to us as a certain facts. So it's really important to provide robust protocols in place and I think having diversity in the teams will facilitate implementation of these protocols.
My pride is to be involved in very different projects since I arrived in the Group. Of course, on the one hand, there are my data science projects, but if we want to, we can go further. For example, I was in charge of the Group's data science community by organizing internal events such as sharing sessions and workshops, but also externally, by organizing meetings with others companies in order to share our best practices with our peers.
Knowledgeable in machine learning and deep learning (NLP, OCRisation, etc)
Experienced in data visualisation and results-oriented
Data Visualisation / results-oriented
Interested in problem-solving/mathematics
Master’s from an engineering school, master’s in data science, (applied) mathematics or IT
The Data Scientist, a data professional specializes in analyzing the complex data collected by the company to anticipate the evolution of customer needs. They are responsible for managing this data to make recommendations and predictive models, i.e., statistical models that allow for hypotheses and predictions.
By using statistical techniques and specific computer tools, they ensure a good mastery of machine learning algorithms. Thus, they help solve the company's business problems and participate in its strategic decision-making.
A Data Scientist's job description includes the following tasks:
Becoming a Data Scientist requires acquiring solid and diverse technical skills.
Indeed, they must have a strong understanding of mathematical principles, particularly in statistics.
Technically, several requirements are expected :
Finally, good communication skills are also highly valued: they help understand client needs and effectively convey the results obtained to the company's management.
To become a Data Scientist, a Master's degree (Bac +5) is required.
After obtaining a Bachelor's degree (Bac +3) in fields like computer science or mathematics, it is advisable to pursue a Master's in Data Science (specialized, in Big Data, or in artificial intelligence) from an engineering school, computer science school, or certain universities.
If you wish to specialize in a specific area, specific training and certifications are also possible.
Finally, to stand out from other candidates, it is beneficial to gain as much professional experience as possible: a data scientist internship or a work-study program will always be appreciated by recruiters.
A Data Scientist can advance to positions with more responsibilities, such as:
They can also choose to specialize in fields such as artificial intelligence or Big Data. Finally, they can move towards roles like Business Analyst, lT Architect, or Software Engineer.
In a typical day, the Data Scientist participates in meetings with various stakeholders within the company to better understand their needs. They collect and clean data to analyze it optimally. Finally, they test the different models they develop and evaluate their performance.
The average salary of a Data Scientist varies based on their experience, geographic location, and the industry sector of the company they work for. For example, in France, a junior Data Scientist can earn an average of between €40,000 and €50,000 per year, while a more experienced Data Scientist, also known as a senior, can earn over €70,000 per year.
A Data Scientist analyzes data and develops predictive models. A Data Engineer, on the other hand, manages and ingests data (the process of importing large volumes of data into a single storage system) to build and maintain the infrastructure necessary for data collection.
A Data Scientist works on advanced data analysis and develops predictive models to meet specific business needs, while a Data Analyst analyse des données pour établir des recommandations permettant la prise de décision stratégique de l’entreprise.
To get to know each other and make sure you will be happy as part of Societe Generale, please follow the below steps.
01
Get in touch
By responding to our job advertisement.
02
Tell us a little bit more about yourself
We’ll contact you for an initial exchange and online tests.
03
Is it a match?
You’ll meet our operational team and human resources partners.
04
Welcome!
The job is yours, and you’re ready to start the adventure.
At Societe Generale, we make sure that you can develop your career based on your ambition and abilities. After a position as a Data Scientist, you will have access to new opportunities such as:
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