Senior Data Scientist - Pricing

Veepee

Pioneer of online flash sales since 2001 and key player in European e-commerce, Veepee collaborates with over 7,000 brands to offer highly discounted products available for a limited time. Operating across various sectors, including fashion, home, wine, travel or beauty... Veepee achieved a turnover of 3.3 billion euros incl. VAT in 2024 and employs 5,000 staff members across 10 countries. 🔭 Job Description   Veepee is seeking a talented Senior AI Engineer to join its VeepeeTech team and drive the development and deployment of Generative AI solutions to enhance user experience (hyper personalization, search), sales and operational efficiencies (pricing mechanics, fraud detection, sales and media production, …)   This role will contribute to end-to-end AI projects, from requirement qualification, business stakeholder exchange, and solution formalization, to data ingestion, potential model finetuning, project delivery, production deployment, and continuous monitoring while collaborating closely with cross-functional partners (product managers, developers, architects, data scientists, DevOps)   The ideal candidate will have strong hands-on experience with NLP/LLM architectures, modern deep learning frameworks, and deploying on-premise infrastructure while leveraging cloud-based LLM services; a solid foundation in statistics and mathematics with a genuine passion for modeling and problem formulation; significant professional experience on at least one concrete project involving the use of LLMs; and a proven track record of shipping AI models to production at scale.

Vestiaire Collective

Vestiaire Collective is the leading global platform for desirable pre-loved fashion and a pioneer in transforming how people consume fashion.   Our mission is simple: make circular fashion the norm, not the exception. Through technology, expertise, and a highly engaged global community, we enable millions of people to buy and sell fashion in a more sustainable way.   Founded in Paris in 2009, Vestiaire Collective is now a globally scaled marketplace with offices in Paris, London, Berlin, New York, Singapore, and Ho Chi Minh City, and logistics hubs across Europe, Asia, and the US. Today, we are a team of around 600 people from over 50 nationalities, united by a shared ambition: to drive meaningful change in the fashion industry.   Our values, Activism, Transparency, Dedication, Greatness, and Collective, shape how we build, collaborate, and grow every day. About the Role We are seeking a Foundational Machine Learning Engineer for a high-impact greenfield opportunity to build our MLOps infrastructure from the ground up at Vestiaire Collective. While driving our AI authentication initiatives (deploying multi-model approaches including computer vision for luxury product authentication and counterfeit detection) will be your immediate focus, your long-term mission will be to scale foundational architecture across the entire marketplace. You will expand our ML capabilities to power broader domains, primarily focusing on search and recommendation systems, with future expansions into dynamic pricing and marketing technologies. Acting as the bridge among Applied Science, Data Platform, and Backend Engineering, you will design robust, decoupled architectures and spearhead the MLOps strategy with our Director of Data, prioritizing system maintainability, engineering hygiene, and the reliable deployment of complex models, ensuring all our ML models across the board deliver high-throughput, low-latency business impact. What You Will Do Short-Term Impact (First 6 Months): Partner closely with the Operations squads and Data Scientists to accelerate ML and RAG prototypes into resilient, production-ready code. You will directly integrate with the team to deploy, optimize, and scale heavy-width CV and VLM models focused on fraud detection and luxury product authentication, immediately improving our trust and safety ecosystem. Mid-Term Foundation (MLOps Lifecycle & Infrastructure): Lead the end-to-end foundational groundwork of our ML lifecycle by designing robust systems for Data & Feature Management, Model Tracking & Registry, and Model Serving & Monitoring. You will scale infrastructure by automating continuous retraining pipelines that handle diverse deployment cadences (from daily fraud detection to weekly recommendations), design resilient multi-model architectures, and critically evaluate the technical overhead and TCO of our in-house tools against enterprise-grade platforms to ensure long-term resilience. Long-Term Vision (Centralizing 360-Degree MLE Capabilities): Act as a pioneer and cornerstone hire for the ML engineering discipline at Vestiaire Collective, setting the technical standards to help scale the AI/ML organization. You will transition into a centralized foundational role, moving beyond single-squad operations to mentor the team and provide horizontal ML infrastructure support to multiple domains, including Search, Discovery, Pricing, Marketing, and Data Platforms. Who You Are Must-Haves: Experience: 5-8+ years of hands-on experience in Machine Learning Engineering, specifically focused on building and scaling MLOps infrastructure and productionizing ML systems. Production Infrastructure: Proven expertise in deploying low-latency, high-throughput ML inference services (using FastAPI, TorchServe, Triton Inference Server, or Ray Serve) across both classical lightweight and heavy-width ML models (PyTorch/TensorFlow). Strong preference for AWS (EKS, EC2, SageMaker) / Snowflake and Open Source ecosystems over GCP/Azure. MLOps & Pipelines: Deep experience building automated, continuous model retraining pipelines to handle concept drift (ranging from daily to weekly cycles). You have orchestrated decoupled, multi-model AI architectures using tools like Airflow, Kubeflow, or Metaflow, and possess strong expertise in model registry and tracking tools like MLflow or Weights & Biases. Feature Stores: Hands-on experience evaluating, building, or extensively leveraging online (Redis, DynamoDB) and offline (Snowflake, S3) Feature Stores in a production environment. Familiarity with frameworks like Feast or custom dbt-based pipelines is highly valued. Strategic Builder Mindset: You are an analytical builder who thinks long-term. You can successfully evaluate TCO for bespoke internal systems versus enterprise tools, anticipate technical liabilities, and design robust architectures that handle unpredictable peak traffic surges. Collaboration & Engineering Hygiene: Strong cross-functional communication skills. You excel at translating complex ML prototypes into highly scalable production code backed by strict version control, rigorous testing, and CI/CD best practices, seamlessly connecting data science innovation with backend engineering execution. Nice-to-Haves: Relevant Domain Expertise: Background in E-commerce, Single-SKU Marketplaces, Search & Recommendation, Trust & Safety, or Counterfeit Detection. Vision, Edge & Optimization: Hands-on experience with Vector Databases, Visual RAG pipelines, deploying Deep Learning VLM models, and optimizing models for edge computing or low-latency inference (e.g., ONNX, TensorRT). Infrastructure & Observability: Advanced experience with containerization (Docker, Kubernetes), Infrastructure as Code (Terraform), and data transformation workflows (dbt). Familiarity with setting up advanced monitoring for model performance, concept drift, and system health (Datadog, Prometheus).

Voodoo

ABOUT VOODOO Founded in 2013, Voodoo is a tech company that creates mobile games and apps with a mission to entertain the world. Gathering 800 employees, 7 billion downloads, and over 200 million active users, Voodoo is the #3 mobile publisher worldwide in terms of downloads after Google and Meta. Our portfolio includes chart-topping games like Mob Control and Block Jam, alongside popular apps such as BeReal and Wizz. TEAM The Engineering & Data team builds innovative tech products and platforms to support the impressive growth of their gaming and consumer apps which allow Voodoo to stay at the forefront of the mobile industry. Within the Data team, you’ll join the Ad-Network Team which is an autonomous squad of around 60 people. The team is composed of top-tier software engineers, infrastructure engineers, data engineers, mobile engineers, and data scientists. The goal of this team is to provide a way for Voodoo to monetize our inventory directly with advertising partners, and relies on advanced technological solutions to optimize advertising in a real-time bidding environment. It is a strategic topic with significant impact on the business. This role can be based in Paris or Helsinki (hybrid mode). ROLE * You will integrate a small, high-ownership squad where you own the full lifecycle - design, implement, deploy, monitor, and iterate - with direct impact on revenue-critical systems processing millions of bid requests per second. * Lead the design and architecture of backend services that power real-time model inference and bidding decisions for our OpenRTB platform. * Collaborate with data scientists and machine learning engineers to deploy, monitor, and optimize ML models that influence real-time bidding strategies, pricing decisions, and targeting - including potentially developing proprietary models in-house. * Oversee the development of A/B testing frameworks and ensure the seamless integration of experimentation tools into our platform for continuous model and bidding optimization. * Ensure that all backend services are high-performance, low-latency, and scalable, capable of handling large data volumes (millions of bidding events per second). * Set best practices for architecture, API design, and distributed systems to ensure robust and maintainable systems at scale. * Work with the cloud infrastructure teams to ensure efficient deployment, scaling, and monitoring of backend services using Kubernetes, Docker, and CI/CD pipelines. * Work closely with product managers to define and implement new features, optimizations, and improvements to the bidding and model inference system. * Lead efforts to optimize performance and cost-efficiency across the backend infrastructure, ensuring that the system can scale effectively with increasing traffic and data. * Continuously monitor the system’s performance, perform post-deployment analysis, and make improvements based on real-world usage and A/B test results. PROFILE (MUST HAVE) * 7+ years of experience in backend engineering, with a strong focus on designing and building scalable, high-performance systems. * Well-versed with machine learning and deploying models for inference in production. * Extensive experience in distributed systems, microservices, and API design using Python. * Hands-on experience with observability tools (e.g., Prometheus, Grafana) for metrics collection, log aggregation, and system monitoring. * Understanding of A/B testing concept and experience integrating them into backend systems for experimentation and optimization. * Proven ability to design, build, and manage cloud infrastructure using Kubernetes, Docker, and cloud-native tooling. Experience with other cloud providers is welcome, but AWS is preferred as it is our primary platform. * Solid experience with CI/CD pipelines, and infrastructure automation tools (e.g., Terraform). * A focus on building reliable, maintainable, and scalable systems, with experience in performance tuning and cost optimization. * Strong problem-solving skills, quality ownership and autonomy: able to take a task from design to production end-to-end - writing contract tests for upstream/downstream interfaces, verifying changes in staging, and treating CI as necessary but not sufficient. NICE TO HAVE : * Experience with OpenRTB and real-time bidding systems. * Expertise in real-time data processing and event-driven architectures (e.g., Kafka, GCP Pub/Sub, Kinesis) for building responsive, low-latency systems. * Genuine interest in understanding the full machine learning lifecycle - from modelization and training to serving and monitoring . * Experience with model serving frameworks (Triton Inference Server, TorchServe, TF Serving, Ray Serve, KServe, or similar) - deploying, configuring, and operating model servers in production. * Exposure to data science workflows and understanding how data engineers and data scientists collaborate in productionizing ML models. * Contributions to open-source projects or active involvement in technical communities related to backend engineering or machine learning. BENEFITS (FOR FRANCE) * Competitive salary upon experience * Swile Lunch voucher * Gymlib (100% borne by Voodoo) * Premium healthcare coverage SideCare, for your family is 100% borne by Voodoo * Child day care facilities (Les Petits Chaperons rouges) * Wellness activities in our Paris office * Remote days