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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Chief Data Officer H/F - **Company:** Solutionbehind Sparteo - **Location:** Paris, France (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** A/B Testing, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Elastic Compute Cloud, Data Analysis, Big Data, Databases, Continuous Integration, Data Architecture, Information Engineering, Data Governance, Data Transformation, Data Retention, Data Warehousing, High-Frequency Trading, Knowledge Management, Machine Learning, Performance Tuning, Raw Data, OpenAI, Data Streaming, Management of Software Versions, AI Infrastructure, Reinforcement Learning, Graphics Processing Unit (GPU), Retrieval-Augmented Generation, Large Language Models, Deep Learning, Generative AI, Agentic-AI, Yield Optimization, Kubernetes, Real-time Inference, Information Technology, Bare Metal, Data Analytics, Machine Learning Operations, Vertica, Claude, Google Gemini - **Published:** October 14, 2026 - **Apply:** https://www.hellowork.com/fr-fr/emplois/84404346.html ## About the Role Adtech / High-Frequency DNA: Experience in very high-volume environments (High-Frequency Trading, Adtech) is crucial for mastering real-time prediction challenges. - Lived scaling experience: Has personally scaled and optimized data and ML infrastructure under tight cost discipline, ideally bare metal and Kubernetes, not only in elastic cloud environments. - Data architect: Able to own architecture decisions end to end. - AI Mastery: Strong expertise in Machine Learning, from model design to real-time inference, with hands-on experience building continuous learning pipelines that keep models accurate and reliable at scale. A plus: deep understanding of Deep Learning, Transformers and GPU-based training and inference. - Advanced AI tooling (mandatory): Builds own skills, plugins and artifacts with LLM assistants (Claude at Sparteo; OpenAI or Gemini equivalents accepted). - Hands-on Expertise: Proven ability to audit code, validate data architectures on self-operated infrastructure and master columnar databases and big data pipelines. - Academic Excellence: Master's or Ph.D. in Mathematics, Statistics or Computer Science. Leadership & Management - Technical Authority: Engineering, AI and mathematics level much higher than the average manager in the sector. - Proven Experience: Confirmed experience as a CDO or VP Data managing multidisciplinary teams (Eng + Science + Analytics). - Leads from the front: Writes POCs, pair-programs, lives the problems with the team rather than delegating from afar. - Culture: Direct, transparent communication and strong alignment with our culture deck. - Change management: Brings along senior experts who built the current systems. - Double Vision: Capable of high-level strategy while retaining the ability to deep-dive into technical specificities. Your mindset to share our adventure - You want to make an impact and move things forward collectively, building on what works and questioning what can be improved. - You solve problems pragmatically and analytically. - You're looking for a fast-moving environment where your agility will be an asset. The 80-20 (Pareto) principle holds no secrets for you. - Your ability to listen encourages you to challenge and improve yourself on an ongoing basis., postgraduate degree EducationalOccupationalCredential bachelor degree EducationalOccupationalCredential associate degree ## Description Our client-facing products and real-time analytics are best in class. The first mission is to bring the same level to our internal teams (Sales, CSM): internal data APIs, unified dashboards and one shared set of KPI definitions. The CDO leads the entire Data & AI function and works hand in hand with the CTO. This is not an administrative role, but that of a leader with strong scientific and mathematical authority, able to understand and challenge the work of senior data scientists and engineers, and to support them on the toughest problems. The goal is not to out-code the team but to structure it, set direction and act as a mirror. You will orchestrate the synergy between infrastructure, Artificial Intelligence (including Generative AI) and Business Intelligence. Strategic Objectives & Vision - Restructuring: Structure the Data department to support Sparteo's growth. - Architect Vision: Define the organizational building blocks (Heads/Leads per pillar) to elevate global technical maturity. - AI Roadmap: Define and execute the strategy for integrating LLMs and Agentic AI to automate yield optimization and internal processes, including our internal data agent and conversational analytics for clients (RAG, text-to-API). - Single source of truth: One set of KPI definitions, formulas and naming across the group. - Data governance & compliance: Own data governance across the group, including GDPR compliance in partnership with our DPO, data retention and minimisation. - Documentation & knowledge management: Help a strong team move from largely oral knowledge to written, durable processes. - Budget ownership: Manage the Data & AI annual budget and decide where to automate and where to hire. - Representation: Carry the voice of Data and AI to the board, investors and external partners. - Holistic Management: Lead the Heads/Leads of each pillar to ensure total coherence of the Data/AI value chain. The Three Pillars Under Your Direction 1. Data Engineering & AI Infrastructure (The Real-Time Backbone) - High-Performance Infrastructure: Validate technological choices to absorb massive volumes of ad-requests with minimal latency, on self-operated, lean infrastructure under tight cost discipline. - Production Pipelines: Ensure the availability of critical data flows for bidding and monetization and real-time AI inference. - Standardization & Stack: Own the data stack (ClickHouse, SQLMesh, Lightdash, Kubernetes on bare-metal infrastructure operated in-house) and CI/CD processes for seamless scalability. - Internal data platform: Own the evolution of our data warehouse, expose internal data APIs with proper access controls, and make sure our internal data agent queries the warehouse rather than raw data. - Cross-functional steering: Co-lead with the CTO the weekly review on infrastructure, pipelines, AI and resource consumption. 2. Data Science (The Algorithmic Heart & MLOps) - Algo Wars & Yield: Drive innovation in yield management, floor pricing, throttling, bidding optimization (including bandit approaches) and audience scoring via advanced predictive models and reinforcement learning. - Scientific Credibility: Possess a high-level mathematical background to guide the team (3 people). - ML Lifecycle (MLOps): Validate strategies for auto-tuning, versioning and A/B testing of algorithms within a constrained production environment, including experimentation designs where classic A/B testing reaches its limits. - Team structuring: Structure the team's roadmap, objectives and follow-up, and bring fresh perspectives to strong, autonomous experts. - Audit: Audit mathematical models and maintain a standard of excellence. 3. Data Analytics & BI (The Business Engine) - Insights Strategy: Transform raw data and AI-generated insights into growth levers and revenue strategies for Sales and CSM teams. - Data product approach: Data contracts, versioning and internal APIs feeding dashboards and agents. - Access rights: Own the KPI access-rights policy with business owners, each business line remaining owner of its data. - Scale through automation: Scale analytics through the internal data agent rather than headcount. - Partnership: Act as a privileged point of contact for executive management and investors. - High-Fidelity Reporting: Guarantee the accuracy of dashboards (supervision of one person).