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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Data Scientist - AI & Data Warehouse Systems - **Company:** EXANTE - **Location:** France (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Data Analysis, Business Logic, BigQuery, Databases, Data Governance, Data Infrastructure, Information Leak Prevention, Data Warehousing, Machine Learning, Standard Sql, SQL Databases, Large Language Models, Snowflake, Multi-Agent Systems, Data Analytics, GPT, Automation Anywhere, Amazon Redshift, Databricks - **Published:** May 24, 2026 - **Apply:** https://fr.indeed.com/viewjob?jk=3d06c78710d2cc12 ## About the Role Do you have experience in SQL?, * Strong SQL and analytical data modeling * Experience working with analytical databases (BigQuery, Snowflake, Databricks, Redshift or similar) * Practical experience with LLMs (ChatGPT, Claude, OpenAI APIs or similar) * Experience building AI-driven workflows or agent-based systems * Strong analytical and problem-solving skills * Understanding of machine learning fundamentals Nice to have: * Experience training or fine-tuning models using historical/labeled data * Entity resolution / record matching * Classification models and probability scoring * RAG / vector databases * dbt / semantic layers * BigQuery optimization * Finance, brokerage, or fintech experience * Data governance or fine-grained access control ## Description We are looking for a Senior Data Scientist to help build the next generation of analytics on top of our enterprise data warehouse. This role is focused on a practical and increasingly important challenge: how AI interacts with structured business data. The successful candidate will help design and build systems where AI can understand, query, validate, reconcile, and reason on analytical data in a reliable and controlled way. This is not a traditional dashboarding role, and it is not a pure research-focused Data Science role. The primary focus is practical implementation of AI systems inside a modern data warehouse environment. The role combines several disciplines: * Data Science * AI / Large Language Models (LLMs) * Data Warehousing & Analytics Engineering * Machine Learning * Data Quality & Validation * AI Agentic Workflows What We Are Building We are building an AI-ready analytical environment on top of our data warehouse where AI can reliably interact with structured business data. Examples include: * Natural-language analytics on warehouse data * AI-driven reconciliation and validation * Analytical copilots * Data quality automation * Business metric validation * Automated anomaly detection * Multi-agent analytical workflows, Help define how AI interacts with warehouse data through prompts, semantic context, retrieval logic, validation mechanisms, and AI workflows. Ensure AI produces correct and trusted answers., Work hands-on with technologies such as ChatGPT, Claude, OpenAI APIs, and AI agents. Test, evaluate, and improve AI outputs. Design workflows where multiple AI agents collaborate to solve analytical problems. 4. Ensure Metric Correctness Help define how AI interprets business metrics and financial logic. Ensure AI correctly understands the difference between concepts such as revenue, cost, gross revenue, net revenue, trading volume, cash movement, accounting movement, balances, and position changes. 5. Improve Data Governance & Reliability Ensure AI interacts with data in a controlled and secure manner. Help implement validation mechanisms, prevent sensitive data exposure, and improve trust in AI-generated outputs. Scope: Data Platform * Build clean, reusable analytical data layers in BigQuery * Move business logic from BI into the warehouse * Define metrics, dimensions, and semantic consistency AI Interaction * Enable reliable AI data interaction * Design schemas + instructions so AI produces correct outputs * Test and refine real AI usage (not theory) Access & Governance * Implement data-layer access control (not BI-layer) * Row/column-level security, role/attribute-based access * Ensure consistent behavior across BI, AI, and internal tools * Ensure metric reconciliation across different data sources Prevent: * sensitive data leakage * shadow metric layer * uncontrolled query cost Automation: * Replace manual data workflows with AI-driven processes * Build agents for reporting, validation, and internal analytics ## Related Videos - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Kubernetes and Microservices with Multi-Model Databases](https://www.wearedevelopers.com/videos/382-kubernetes-and-microservices-with-multi-model-databases) - [ Evaluating AI models for code comprehension](https://www.wearedevelopers.com/videos/1462-evaluating-ai-models-for-code-comprehension) - [Data Governance in the Era of AI](https://www.wearedevelopers.com/videos/1622-data-governance-in-the-era-of-ai) - [Making Data Warehouses fast. 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