11 of 11 Model Validation Jobs in Central London

driven solutions within a complex, enterprise-scale environment. This is an exciting opportunity to lead testing strategy across cutting-edge AI systems, ensuring robust validation, strong governance, and continuous quality improvement across digital platforms. The Role As a Senior Test Engineer, you will operate at a strategic level, shaping … testing approaches for AI and data services while remaining hands-on across automation, validation, and delivery pipelines. You will: Lead the design and execution of end-to-end test strategies across AI and digital programmes Ensure quality across AI models, data pipelines, APIs, and cloud-based services Embed test ...

instruments across trading and clearing platforms. Monitor and validate quantitative models, valuation outputs, and market inputs to ensure robustness and consistency. Oversee the collection, validation, and maintenance of market data from external venues, including the calculation and governance of market indices. Develop and enhance volatility surface construction, interpolation ...

environments. Seeking Experience in most of the following: Python or similar scripting languages for data exploration and preparation. Machine learning and statistical methods, including model validation. Data analytics and visualisation techniques. Processing large datasets using batch or streaming tools such as Apache Spark. Data acquisition and fusion approaches. Cloud ...

environment. The Data Scientist role suits a hands-on Data Scientist who is comfortable working across the full lifecycle from data exploration through to model development validation plus deployment support. Responsibilities for the Data Scientist: Develop statistical models machine learning solutions analytical frameworks to address complex business problems … outputs into clear insights recommendations for technical non-technical stakeholders Work closely with data engineers analysts to ensure models are production-ready scalable Validate model performance manage bias ensure outputs are robust explainable reliable Contribute to continuous improvement of data science practices tooling governance Essential Skills for the Data ...

integrate AI, RAG, and machine learning capabilities into enterprise applications. Own the end-to-end AI/ML lifecycle, including problem definition, data exploration, model development, validation, deployment, and monitoring. Develop forecasting models, segmentation strategies, optimisation algorithms, and early warning systems to support sales performance and decision-making. … engineering, sales operations, and business stakeholders to embed intelligent capabilities into reporting and analytics platforms. Lead technical decision-making related to AI infrastructure, governance, model monitoring, and safety controls. Conduct experimentation, statistical analysis, and causal inference studies to evaluate business outcomes and solution effectiveness. Mentor team members and contribute ...

London Stock Exchange’s platform Real-Time Distribution System & Real-Time Managed Distribution System Transformation of the platform to a hybrid cloud model Packaging & validating multiple vendor products on our various infrastructures – cloud/on-prem (GCP/Anthos/Bare metal) Configuring & tuning market data infrastructure products ...

Marketing Mix Modelling projects using frameworks such as Meridian, Robyn, or PyMC Own key stages of the modelling process, including data preparation, model development, validation, and scenario analysis Interpret model outputs and translate them into clear, actionable insights for clients Work directly with stakeholders to support strategy … Solid understanding of statistical modelling techniques, including Bayesian approaches Experience delivering analytical work in a client-facing or stakeholder-driven environment Ability to assess model quality and clearly communicate findings to non-technical audiences Exposure to marketing analytics, attribution, or media effectiveness projects is highly beneficial What They Offer ...

Senior Data Analyst £55,000 London, UK (Hybrid) Our client is a fast-growing data and media technology business using advanced analytics to power smarter campaign strategy and execution. By combining audience insight, machine learning ...

Analyst with strong experience in machine learning system implementation, for a 6 month contract (with potential for extension) based in the London office (hybrid model, 2 days a week in the office). Purpose of the role: The Business/Data Analyst will support the implementation of our client … Machine Learning-driven AML platform. This role sits within the Product team and will play a key role in defining requirements, supporting model development and ensuring effective financial crime detection outcomes within a fast-paced agile programme. What you'll do: Gather and document business and AML requirements. Work ...

Company Description Deep Optica is an AI4Science startup building a Mining World Model to transform how subsurface resources are discovered and evaluated. The World Model integrates geological, geophysical, geochemical, remote sensing, and drilling data to represent subsurface conditions and explicitly capture uncertainty. It is delivered through expert … Area, United Kingdom, with flexibility for partial work from home. In this position, the Chief Geoscientist leads the scientific direction of the Mining World Model, designing frameworks to integrate geological, geophysical, and geochemical datasets into robust subsurface representations. Day-to-day responsibilities include overseeing interpretation workflows, guiding data quality ...

rather than handing the modelling to someone else. Product & direction • Define and own the product vision, strategy, and roadmap for our Animal Health market model – spanning historical baselining, predictive analytics, and forward forecasting (e.g. demand, market sizing, and market share evolution). • Translate commercial questions from sales and marketing … external market sources, including the judgement calls when sources are imperfect. Stakeholders • Be the bridge between the commercial business, data science, and engineering – communicating model logic, assumptions, and limitations in language each audience trusts. Essential Strong applied proficiency in Python and/or R, including modern data ...