Lead Software Engineer

Reference 26000FXD

Responsibilities

Lead ML Analyst – Wholesale Banking

Role Summary

The Lead ML Analyst is a senior individual contributor responsible for connecting Wholesale Banking business needs with Data Science, Machine Learning, and Generative AI solutions.

The role drives business engagement, requirement gathering, prioritization, capacity planning, and successful adoption of AI/ML initiatives while working closely with business stakeholders, Data Scientists, and ML Engineers.

Mandatory Skills

• Strong communication, stakeholder management, and business consulting skills.
• Ability to gather, structure, and translate business requirements into AI/ML use cases.
• Deep understanding of one or more Wholesale Banking domains with end-to-end process knowledge.
• Expertise in demand management, prioritization, capacity planning, and project coordination.
• Ability to understand the digital strategies of organizations and objectives of the group/business lines, align the solutions accordingly.
• Good understanding of LLM architectures, GenAI concepts, and prompt engineering techniques.
• Strong data visualization, storytelling, and insight-generation capabilities.
  
  

Additional Skills

• Working knowledge of Python and common data science libraries.
• Good understanding of Machine Learning concepts and practical applications.
• Familiarity with SDLC, CI/CD pipelines, APIs, and deployment practices.
• Knowledge on Streamlit, dashboards, and user-facing analytical applications.
• Good understanding of different Retrieval-Augmented Generation (RAG) architectures.
  
  

Optional Knowledge

• Exposure to Kubernetes and Airflow.
• Familiarity with Azure AI services and cloud-based analytics platforms.
• Awareness of Agentic AI frameworks such as LangChain and LangGraph.
  
  

Understanding of MLOps and production AI/ML deployment practices.

Additional Skills

Profile required

• Working knowledge of Python and common data science libraries.
• Good understanding of Machine Learning concepts and practical applications.
• Familiarity with SDLC, CI/CD pipelines, APIs, and deployment practices.
• Knowledge on Streamlit, dashboards, and user-facing analytical applications.
• Good understanding of different Retrieval-Augmented Generation (RAG) architectures.
  
  

Optional Knowledge

• Exposure to Kubernetes and Airflow.
• Familiarity with Azure AI services and cloud-based analytics platforms.
• Awareness of Agentic AI frameworks such as LangChain and LangGraph.
  
  

Understanding of MLOps and production AI/ML deployment practices.

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.

We are committed to support accelerating our Group's ESG strategy by implementing ESG principles in all our activities and policies. They are translated in our business activity (ESG assessment, reporting, project management or IT activities), our work environment and in our responsible practices for environment protection.