Lead Data Engineer

Responsibilities
A seasoned Data Scientist with 6-7 years of professional experience. This role offers the opportunity to leverage expertise in statistical analysis and AI/ML to develop impactful solutions that align with our enterprise strategy. Data Scientist will be deeply involved in the entire project lifecycle—from data preparation and exploratory analysis to model deployment—while collaborating with multidisciplinary teams to deliver scalable, measurable results.
Key Responsibilities:
Develop and implement high-impact AI/ML use cases that support our organizational objectives.
Communicate findings, insights, and methodologies clearly to non-technical stakeholders.
Design, build, and optimize predictive models, classifiers, and regression algorithms using classical AI/ML techniques such as SVMs, Decision Trees, Random Forests, k-NN, Naive Bayes, and ensemble methods.
Validate models with appropriate statistical and machine learning evaluation techniques.
Apply strong statistical foundations, including distributions, hypothesis testing, regression analysis, and probability theory.
Conduct thorough exploratory data analysis to uncover key trends, patterns, and anomalies.
Ensure data quality and reliability through rigorous analytical practices.
Support the ML lifecycle, including model design, infrastructure, production setup, monitoring, and release management, with basic familiarity in MLOps.
Requirements:
6-7 years of hands-on experience in Data Science.
Proven proficiency in statistical data analysis, machine learning, and natural language processing, with a strong understanding of practical constraints.(Must Have)
Advanced skills in Python programming and SQL, utilizing relevant libraries for effective data analysis. (Must Have)
Demonstrated experience in AI/ML solution development, including supervised and unsupervised learning algorithms, model evaluation, and feature engineering. (Must Have)
Basic familiarity with MLOps and feature engineering methods for model workflows.(Basic Knowledge)
Competency in software development methodologies and versioning tools. (Must Have)
Experience with front-end visualization tools such as Streamlit or lightweight UI layers (Good to Have).
Exposure to GenAI, including LLM integration, prompt engineering, model packaging, and lifecycle management (Preferred).
Familiarity with agentic AI frameworks like LangChain and LangGraph, and agent-based patterns (Good to have
Required
Profile required
A seasoned Data Scientist with 6-7 years of professional experience. This role offers the opportunity to leverage expertise in statistical analysis and AI/ML to develop impactful solutions that align with our enterprise strategy. Data Scientist will be deeply involved in the entire project lifecycle—from data preparation and exploratory analysis to model deployment—while collaborating with multidisciplinary teams to deliver scalable, measurable results.
Key Responsibilities:
Develop and implement high-impact AI/ML use cases that support our organizational objectives.
Communicate findings, insights, and methodologies clearly to non-technical stakeholders.
Design, build, and optimize predictive models, classifiers, and regression algorithms using classical AI/ML techniques such as SVMs, Decision Trees, Random Forests, k-NN, Naive Bayes, and ensemble methods.
Validate models with appropriate statistical and machine learning evaluation techniques.
Apply strong statistical foundations, including distributions, hypothesis testing, regression analysis, and probability theory.
Conduct thorough exploratory data analysis to uncover key trends, patterns, and anomalies.
Ensure data quality and reliability through rigorous analytical practices.
Support the ML lifecycle, including model design, infrastructure, production setup, monitoring, and release management, with basic familiarity in MLOps.
Requirements:
6-7 years of hands-on experience in Data Science.
Proven proficiency in statistical data analysis, machine learning, and natural language processing, with a strong understanding of practical constraints.(Must Have)
Advanced skills in Python programming and SQL, utilizing relevant libraries for effective data analysis. (Must Have)
Demonstrated experience in AI/ML solution development, including supervised and unsupervised learning algorithms, model evaluation, and feature engineering. (Must Have)
Basic familiarity with MLOps and feature engineering methods for model workflows.(Basic Knowledge)
Competency in software development methodologies and versioning tools. (Must Have)
Experience with front-end visualization tools such as Streamlit or lightweight UI layers (Good to Have).
Exposure to GenAI, including LLM integration, prompt engineering, model packaging, and lifecycle management (Preferred).
Familiarity with agentic AI frameworks like LangChain and LangGraph, and agent-based patterns (Good to have
Why join us
“We are committed to creating a diverse environment and are proud to be an equal opportunity employer. All qualified applicants receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status”.
Business insight
At Société Générale, we are convinced that people are drivers of change, and that the world of tomorrow
will be shaped by all their initiatives, from the smallest to the most ambitious.
Whether you’re joining us for a period of months, years or your entire career, together we can have a positive impact on the future. Creating, daring, innovating and taking action are part of our DNA.
If you too want to be directly involved, grow in a stimulating and caring environment, feel useful on a daily basis and develop or strengthen your expertise, you will feel right at home with us!
Still hesitating?
You should know that our employees can dedicate several days per year to solidarity actions during their working hours, including sponsoring people struggling with their orientation or professional integration, participating in the financial education of young apprentices and sharing their skills with charities. There are many ways to get involved.

Work arrangement
No