Datascientist

About Rothschild & Co

Rothschild & Co is a leading international financial services group, controlled by the family for seven generations and at the heart of financial markets for over 200 years.

Our expertise, know-how, and global network allow us to offer a unique vision that benefits the business and wealth of our clients and stakeholders, while respecting our planet.

Our teams of 4,200 financial advisory specialists on the ground in over 40 countries bring a unique international perspective across four leading activities – Global Advisory, Wealth Management, Asset Management, and Five Arrows.

As a family-run group built on human relationships, we place particular importance on recruiting and developing our employees' careers so they can thrive professionally.

Role Overview

We are looking for a Data Scientist to join the WAM AI Center of Expertise (CoE), a cornerstone of the Data & AI strategy for Wealth & Asset Management (WAM). The CoE's mission is to implement the AI roadmap at the WAM level across all its geographies (including France, Belgium, Monaco, UK, Switzerland), pool expertise and resources, and ensure a coherent, compliant, and high-performing approach for all existing AI solutions for business processes.

Operating within a distributed and coordinated model, the CoE ensures cross-functionality, standardization of practices, and governance of solutions. It supports business units in identifying, prioritizing, and designing new use cases, while also supporting their large-scale deployment in close collaboration with business, IT, and support teams.

It comprises Product teams, responsible for designing and developing AI solutions that directly meet business needs. These Product teams are supported by Platform teams, which define and manage the technical roadmap, maintain environments, tools, and cross-functional services (Azure AI Foundry, APIM, DevOps, observability, security), and continuously work to automate, optimize, and industrialize processes.

The CoE Data Scientist contributes to the design, industrialization, and performance of generative AI solutions for private banking and asset management. This role involves strong collaboration with Data Engineering, Product Owners, and business teams to ensure the performance, compliance, and scaling of AI use cases.

Main Missions:

Data Collection, Preparation, and Analysis

• Collect, structure, and validate data from multiple sources (internal databases, APIs, open data) while adhering to quality and compliance standards.
• Analyze and interpret data to provide strategic insights and recommendations.

Modeling and Generative AI

• Develop, train, and optimize AI models (LLM, RAG, fine-tuning) for strategic business use cases.
• Develop, train, and deploy machine learning and deep learning models (classification, regression, clustering, time series, NLP, computer vision, etc.).
• Integrate advanced techniques (quantization, distillation) to improve performance and reduce costs.

Industrialization and MLOps

• Collaborate with Data Engineers to deploy models into production (CI/CD, monitoring, cloud).
• Ensure the robustness and scalability of AI solutions in a secure environment.

Governance and Compliance

• Contribute to the implementation of AI governance standards (quality, security, GDPR).
• Document models and ensure traceability of data and decisions.

Innovation and Adoption

• Identify new AI use cases in collaboration with business units and sponsors.
• Participate in technological watch and experimentation (PoC, prototyping).

Communication and Simplification

• Create data visualizations (dashboards, data storytelling) to communicate results clearly and impactfully.
• Educate business teams on best AI practices.

Expected Experience and Skills:

• Minimum of 5 years of experience as a Data Scientist.
• Proficiency in data analysis, machine learning, and model deployment.
• Proficiency in Python, R, PySpark.
• Experience with Azure OpenAI Service and Azure Document Intelligence.
• Knowledge of Microsoft Fabric.
• Proficiency in statistical methods and modeling techniques.
• Skills in data quality management, security, and governance.

Soft Skills:

• Strong analytical mindset, ability to solve complex problems.
• Technological curiosity, desire to contribute to an innovative project.
• Collaborative spirit, autonomy, agility, experimentation capability.