Generative AI Data Scientist (Permanent, Full-Time)

Job description

Rothschild & Co

Rothschild & Co is a leading international financial services group, family-controlled for seven generations and at the center of financial markets for more than 200 years. Our expertise, know-how, and global network enable us to offer a distinctive perspective that benefits our clients’ businesses and wealth, as well as our stakeholders, while respecting our planet. Our 4,200 financial advisory specialists work on the ground in more than 40 countries and provide a unique international perspective through four leading businesses: Global Advisory, Wealth Management, Asset Management, and Five Arrows. As a family group founded on human relationships, we place particular importance on recruiting and developing our employees so they can thrive in their careers.

Context

Within Wealth & Asset Management, the Artificial Intelligence Center of Expertise supports the development of innovative solutions for private banking and asset management. The team works throughout AI use-case lifecycles, from identifying opportunities through experimenting, validating, and deploying concrete solutions.

In a demanding international environment, the AI Center of Expertise plays a key role in accelerating adoption of generative AI while ensuring its use is controlled, secure, and compliant with the standards of a leading financial group.

You will join a multidisciplinary team working closely with business teams, Data teams, platform teams, Security, and Compliance to turn operational needs into useful, robust, industrializable AI solutions.

Role

As a Data Scientist specializing in generative AI, you will contribute to designing, evaluating, and industrializing high-value AI solutions for Wealth & Asset Management. You will work from understanding business needs through validating models and prototypes, in collaboration with technical and functional stakeholders.

Main responsibilities

• Design, develop, and test generative AI solutions for concrete business needs: conversational assistants, retrieval-augmented generation systems, document-extraction tools, analysis workflows, or decision-support tools.
• Prepare, structure, and analyze data for AI use cases, ensuring quality, relevance, traceability, and compliance.
• Define and implement evaluations for models and generated responses: functional tests, performance measurement, analysis of hallucination risk, robustness, and business relevance.
• Work with Data Engineering and platform teams to industrialize AI solutions: integration, automated deployment, monitoring, observability, environment management, and production readiness.
• Document models, datasets, architecture choices, known limitations, and evaluation results to ensure traceability and sound governance.
• Support business users in qualifying needs, defining use cases, interpreting results, and adopting AI solutions.
• Track technology developments, run experiments and proofs of concept, and identify new opportunities to use generative AI.

Expected skills

• Confirmed data science experience, ideally in banking, finance, consulting, or a highly regulated environment.
• Strong Python skills and command of core data science, machine learning, and natural-language-processing libraries.
• Good understanding of traditional machine-learning models and related statistical principles: classification, regression, clustering, time series, and NLP.
• Experience or strong understanding of generative AI architectures: LLMs, RAG, prompt engineering, prompt evaluation, information extraction, and orchestration of AI chains or workflows.
• Knowledge of LLM optimization methods such as fine-tuning, quantization, distillation, or equivalent techniques is valued for industrial use cases.
• Good understanding of Deep Learning fundamentals and agentic AI systems.
• Experience with Azure OpenAI Service, Azure AI Foundry, or equivalent environments for developing and evaluating AI solutions.
• Knowledge of Azure Document Intelligence, Microsoft Fabric, PySpark, or equivalent cloud data environments is an advantage.
• Understanding of MLOps best practices: versioning, testing, integration and deployment pipelines, monitoring, observability, documentation, and production deployment.
• Strong awareness of data governance, security, confidentiality, compliance, and AI-related risk management.

Candidate profile

• At least 3–5 years of experience in data science, machine learning, or applied AI, with significant exposure to generative AI.
• Ability to understand complex business issues and turn them into concrete, testable, industrializable analytical or AI solutions.
• Methodological rigor, analytical thinking, strong documentation skills, and attention to deliverable quality.
• Enjoys teamwork and collaborates with varied stakeholders: business teams, Product Owners, Data Scientists, Data Engineers, platform teams, Security, and Compliance.
• Independent, technologically curious, pragmatic, and able to experiment quickly while respecting the constraints of a regulated environment.
• Strong communication and teaching skills to make AI topics accessible to non-technical stakeholders and encourage adoption.

One reason for the long-term success of our business is the importance we place on being principled. It is this belief that drives both the way we work with our clients and the way we hire, manage and develop our people—and we look for the same qualities from them in return. Our teams reflect the diverse needs of our clients, businesses and the markets we operate in around the world. We value, and seek to maintain, an eclectic mix of skills and backgrounds—encouraging everyone to come together to share their perspectives for the good of each other, the business and our clients.