6 of 6 Permanent Model Validation Jobs in London

Build scalable data workflows and feature stores for ML applications. Infrastructure & tooling: Containerise applications with Docker, orchestrate with Kubernetes, and deploy securely in AWS. Model governance: Apply best practices for evaluation, drift monitoring, and compliance. Collaboration: Partner with Data Scientists and business stakeholders to translate insights into production-ready ...

Work closely with the product team to prioritise features, refine user stories, and support the full product lifecycle. Assist in functional design, testing, and validation of new features before release. Help maintain internal product documentation, user workflows, and configuration standards. Pre-Sales & Demo Environment Configuration Configure and maintain ...

agentic AI – across research, rankings, submissions and product innovation. Oversee the end-to-end analytics lifecycle, from data acquisition and feature engineering through to model development, validation, deployment and monitoring Ensure AI-driven outputs are explainable, transparent and aligned with Chambers’ standards and reputation for independence Provide strategic … outcomes Collaborate with Product and Technology teams to embed AI and analytics into customer-facing products and internal platforms Establish best practices for experimentation, model governance, performance measurement and ethical AI use Define and oversee evaluation frameworks for LLM and GenAI outputs, ensuring accuracy, consistency, bias mitigation and alignment ...

large‐scale codebase modernisation, and collaboration with Quant, Product, and Engineering teams. You will work on refactoring legacy C++ analytics libraries, improving performance, enhancing model accuracy, and supporting the transition to a modern Azure‐based architecture. This role is ideal for engineers who thrive in complex problem‐solving environments … Cloud & Platform Modernisation · Support the migration of analytics components to Azure cloud services. · Collaborate with platform engineers to ensure seamless deployment and integration. Data & Model Integrity · Validate analytics outputs, pricing models, and risk calculations. · Work closely with Quant teams to ensure model consistency and correctness. Engineering Excellence · Apply ...

models that learn across multiple data modalities and scales, from granular signals through to population-level insights Embeds explainability, uncertainty and causal reasoning into model design, not as an afterthought Partners closely with downstream teams to ensure models can be fine-tuned, deployed and deliver real-world impact Makes … clear, defensible build vs adapt vs buy decisions across models, tooling and external partnerships Sets standards for model evaluation, bench-marking, validation and data quality Represents the organisation externally through publications, open-source contribution or recognised thought leadership What success looks like Reusable AI systems that are actively ...

Company Akkodis is a global leader in engineering, technology, and R&D, harnessing the power of connected data to drive digital transformation and innovation for a smarter, more sustainable future. As part of the Adecco ...