22 of 22 Model Validation Jobs in Central London

Responsibilities: Engage in the validation and approval sign off of the firm's models across Liquidity Risk, Market Risk, and Counterparty Risk models. Challenge model assumptions, implementations, and mathematical formulations. Review and oversee the monitoring of the performance of models including outcomes, verification, and benchmarking. Understand and communicate the risks of model limitations to senior management. Requirements … Education: PhD/Masters in a finance/mathematical/quantitative field Prior Experience: 3-5 years in model validation of liquidity/market/counterparty risk models. Knowledge: Strong understanding and experience working with ILST/VaR models Technical: Python More ❯

gather requirements, design data flows, and help implement pricing models, APIs and analytical capabilities that drive better underwriting decisions. Key Responsibilities: Define and document requirements for pricing system integrations, model deployment and API workflows. Translate pricing models (Excel/Python/R) into technical specifications for developers and data engineers. Collaborate with underwriters and actuaries to improve model governance, version control and reporting. Support UAT, model validation and release testing. Deliver insights and metrics around model performance, quote success and underwriting efficiency. Partner with insurers and MGAs to streamline pricing and underwriting processes through automation and data. Required Skills & Experience: 3+ years in a Business Analyst or Technical Analyst role within insurance, reinsurance or … InsurTech. Experience with pricing or rating systems, underwriting workflows or model integration projects. Strong understanding of insurance data structures, rating factors and model governance. Skilled in SQL and BI tools (Power BI, Tableau, Looker). Comfortable working with APIs, data pipelines and collaborating with developers. Excellent communication and stakeholder management skills. Desirable: Exposure to cloud environments (AWS, Azure More ❯

prem environments. Standardize and harmonize diverse data sources, ensuring metadata quality and FAIR compliance. Integrate multi-modal datasets (genomic, transcriptomic, proteomic, imaging, etc.) into unified data models. Automate data validation, quality control, and lineage tracking. Support analytics, visualization, and machine learning workflows. Contribute to data governance practices covering access, privacy, and lifecycle management. Qualifications Bachelor’s or Master’s More ❯

Quantitative Analyst/Model Developer – IRRBB & CSRBB Strategic Enhancement Initiative 📍 Location: London, Canary Wharf (Hybrid – 3 days in office) 🕒 Duration: 9 months (Full-time Contract) 🏢 Department: Enterprise Risk Management – Centralised Modelling, Analytics & Operations (CMAO) About the Role Seeking a skilled Quantitative Analyst/Model Developer to enhance IRRBB and CSRBB capabilities. Focus on developing, maintaining, and integrating advanced … stability) and validate alignment with the firm’s balance sheet and market expectations. Governance & Oversight Deliverables Document metrics production (QRM) and usage for Risk/Treasury. Prepare materials for model validation, stress tests, audits, and senior management. Resolve oversight challenges in methodology, scenario design, assumptions, and decision linkage. Ensure complete CSRBB and IRRBB coverage and capture all risk … risk measurement's link to management actions (limits, stress testing, ICAAP). Regulatory: Familiarity with EBA/ECB IRRBB & CSRBB requirements. Communication: Exceptional written/verbal skills for documentation (validation, regulatory, senior committees). Why Join This is an exciting opportunity to contribute to a high-impact strategic initiative within a leading financial institution. You’ll work in a More ❯

scale machine learning systems that forecast demand, optimise staffing, and improve operational performance across thousands of venues. Lead projects end-to-end, from data design and modelling through to validation, deployment, and monitoring. Develop AI systems across areas such as computer vision, forecasting, optimisation, and emerging generative or agentic models. Partner with engineers to design scalable ML pipelines, APIs More ❯

role centers on evaluating analytical workflows, modeling standards, experimentation culture, and applied business impact . This position is ideal for someone with a strong background in applied data science, model lifecycle design, and organizational data maturity — capable of analyzing current practices and defining what “best-in-class” looks like for scalable, responsible, and high-impact data science operations. Key … Responsibilities • Practice Maturity Assessment: Evaluate current data science processes, tools, and team structures to determine capability strengths, weaknesses, and improvement areas. • Framework Design: Develop and apply a structured maturity model to assess how data science work is conceived, executed, validated, and scaled. • Model Lifecycle Review: Assess practices across data preparation, feature engineering, model development, validation, monitoring … . • Collaboration & Alignment: Work with AI and Data & AI Architects to connect findings from people, platform, and practice assessments into a unified capability map. • Gap Identification: Identify gaps in model governance, documentation, and model-to-business translation and recommend actionable improvement pathways. • Reporting & Advisory: Produce detailed reports summarizing data science maturity, practice gaps, and recommendations for scaling responsibly More ❯

migration projects, ensuring data accuracy and completeness. Design, develop, and maintain dashboards and reports using Power BI (experience with Tableau is a plus). Perform data modeling, mapping, and validation to support business intelligence and reporting requirements. Communicate complex technical concepts effectively to both technical and non-technical stakeholders. Collaborate with cross-functional teams to identify opportunities for process More ❯

Provide ongoing system support, resolve issues, and perform debugging and testing. • Ensure platform stability and security across multiple environments. Data Management & Quality • Design and manage data models, queries, and validation rules to ensure integrity and reliability. • Support data migration and ensure compliance with governance standards. Collaboration & Documentation • Work closely with stakeholders, project managers, and other IT specialists to translate More ❯

a global Lakehouse platform (Data Lake + OLAP) in AWS Develop scalable data pipelines in Python , working with SDKs and data libs Own end-to-end data modeling , ingestion, validation, and optimization for high-concurrency access Tune performance of cross-region, multi-format data stores (columnar, real-time, etc.) Deliver tailored solutions directly to quants and traders – real impact More ❯

challenges. Collaborate with data scientists to transition prototypes into production-ready systems . Develop and maintain end-to-end ML pipelines for data ingestion, training, testing, and deployment. Optimise model performance, scalability, and reliability using MLOps best practices. Work with large-scale structured and unstructured datasets for model training and validation. Implement model monitoring, versioning, and retraining … e.g., MLflow, Kubeflow, SageMaker, Vertex AI). Experience with data engineering concepts — ETL pipelines, data lakes, and cloud data platforms. Proficiency with cloud services (AWS, Azure, or GCP) for model deployment and orchestration. Knowledge of containerization and orchestration tools (Docker, Kubernetes). Experience integrating ML models into production environments via APIs or microservices. Excellent problem-solving, analytical, and communication … skills. Preferred Qualifications Bachelor’s or Master’s degree in Computer Science , Data Science , Mathematics , or a related field. Familiarity with CI/CD pipelines for ML model deployment. Exposure to natural language processing (NLP) , computer vision , or reinforcement learning projects. Experience working in Agile/Scrum environments. Contract Details Location: Hybrid – London (onsite 2–3 days per week More ❯

Real estate portfolio valuation: Work on developing advanced valuation models for real estate portfolios using internal and external data sources. This includes building predictive models with uncertainty estimates, improving model performance through rigorous evaluation, and creating data pipelines to support modelling and analytics. You’ll also prototype processes downstream of valuation models, such as optimization approaches, to enhance pricing … demand and optimize pricing strategies for goods sold by a portfolio company. You’ll build predictive models that incorporate seasonality and competitive pricing data, while quantifying uncertainty and maintaining model explainability to support robust, transparent decision-making. Your Experience: We’re a small, high-impact team with a broad remit and diverse technical backgrounds. We don’t expect any … Degree in a STEM field (or equivalent experience) with hands-on expertise in a least two of applied statistics, machine learning, forecasting, NLP, or optimization. Experience with uncertainty quantification, model evaluation, and statistical inference is highly valued. Python expertise: Skilled in building data pipelines and ML models using modern libraries across multiple domains: Data science stack: NumPy, pandas/ More ❯

that integrates multi-modal biomedical data, deep learning models, and large-scale population datasets such as UK Biobank and Our Future Health. This is a rare opportunity to lead model development in a setting that bridges scientific rigour with production-grade engineering. Key Responsibilities Design, train, and deploy state-of-the-art machine learning and deep learning models to … factors. Build and productionise end-to-end ML pipelines, from research to deployment. Collaborate with clinicians, engineers, and product teams to translate scientific findings into scalable tools. Contribute to model evaluation, explainability, and validation across diverse data sources. About You PhD in Machine Learning, Computational Biology, Statistics, Bioinformatics, or related quantitative field. Background in cardiovascular, cardiometabolic, or precision … and predictive analytics. Demonstrated experience working with large-scale health, genomic, or biobank datasets (e.g. UK Biobank, All of Us, Our Future Health). Exposure to production deployment and model lifecycle management (MLOps awareness a plus). Strong communicator with the ability to operate between science and engineering teams. Nice to Have Experience integrating multi-omic or imaging data More ❯

scaling the QA function - building out the team, defining best practices and creating the quality mindset. Must have experience: Senior leadership scaling QA functions Testing GenAI-powered systems ML validation Building automation frameworks Data quality engineering What's in it for you: £100,000 base salary 🤑 Hybrid working 🏡 25 days holiday + birthday off 🎂 Private medical and dental ❤️ 🩹 Even More ❯

and preprocess structured and unstructured data from multiple internal and external sources. Perform exploratory data analysis (EDA) to identify trends, patterns, and anomalies. Design and implement data pipelines for model-ready datasets in collaboration with data engineering teams. Apply feature engineering and selection techniques to improve model accuracy and interpretability. Develop and validate machine learning and statistical models … models using appropriate metrics and perform hyperparameter tuning for optimal performance. Convert proof-of-concept models into production-grade pipelines in collaboration with MLOps and engineering teams. Required: Translate model outcomes into actionable insights through clear storytelling and visualizations. Build dashboards and reports using Power BI, Tableau, or Python-based visualization tools. Communicate findings to both technical and non … Partner with business analysts, architects, and domain experts to define use cases and success metrics. Contribute to the enterprise AI roadmap, bringing thought leadership on analytical methodologies. Document methodologies, model logic, and validation results for audit and reproducibility. Participate in Agile ceremonies, sprint planning, and client showcases. If you'd like to discuss this data scientist role in More ❯

data stores using Python, Java, or Node.js backends. Collaborate with architects to define scalable and secure AI service architectures. Experience in the following Implementing AI/ML pipelines for model training, validation, and deployment (using tools such as MLflow, Vertex AI, or Azure ML). Manage model evaluation, drift monitoring, and continuous improvement processes. Optimize inference performance … and cost (e.g., model compression, quantization, API optimization). Ensure compliance with AI ethics, security, and governance standards. Prepare and curate training datasets (structured/unstructured text, images, code). Apply data preprocessing, tokenization, and embedding generation techniques. Work with vector databases (Pinecone, Weaviate, FAISS, Chroma) for semantic retrieval use cases. Partner with business stakeholders to identify and shape More ❯

ethics, and integration, shaping our innovation roadmap. Additionally, you will optimize algorithms with tools like Python, TensorFlow, PyTorch, scikit-learn, and cloud services (e.g., AWS SageMaker), including data analysis, model training, and validation. You will address issues in model accuracy, bias, and integration, complying with data privacy regulations while supporting growth. Finally, you will track AI trends and More ❯

ethics, and integration, shaping our innovation roadmap. Additionally, you will optimize algorithms with tools like Python, TensorFlow, PyTorch, scikit-learn, and cloud services (e.g., AWS SageMaker), including data analysis, model training, and validation. You will address issues in model accuracy, bias, and integration, complying with data privacy regulations while supporting growth. Finally, you will track AI trends and More ❯

ethics, and integration, shaping our innovation roadmap. Additionally, you will optimize algorithms with tools like Python, TensorFlow, PyTorch, scikit-learn, and cloud services (e.g., AWS SageMaker), including data analysis, model training, and validation. You will address issues in model accuracy, bias, and integration, complying with data privacy regulations while supporting growth. Finally, you will track AI trends and More ❯

ETL pipelines, manage cloud-based and on-prem data environments, and integrate diverse datasets from external vendors, trading systems and internal sources. The role also involves implementing automated data validation processes, optimising data quality and performance, and ensuring high availability across all systems. Working closely with the portfolio manager, researchers and engineers, you will enable data-driven decision-making … tech firm Strong hands-on expertise in Python and modern ETL frameworks Experience designing and maintaining cloud-based data pipelines (e.g. AWS, Airflow, Snowflake) Deep understanding of data modelling, validation, and pipeline resilience Familiarity with financial or alternative datasets preferred More ❯

implement ETL workflows to manage data extraction, transformation, and loading into Teradata. Build and maintain risk models for credit, operational, and market risk analysis. Conduct data profiling, cleansing, and validation to ensure accuracy and consistency. Collaborate with stakeholders to gather requirements and deliver data-driven solutions. Perform exploratory data analysis to uncover trends and support risk mitigation. Automate reporting … and dashboard creation using Python and BI tools. Optimize Teradata queries and ETL performance for efficient data processing. Document data flows, model logic, and technical specifications for transparency. Ensure compliance with data governance and contribute to continuous improvement initiatives. Your Profile: 3+ years of experience in data analysis and ETL development. Strong proficiency in Teradata SQL and Informatica PowerCenter. More ❯

with a primary focus on deriving new markets and enhancing existing offerings. Possess a deep understanding of complex statistical distributions and leverage techniques such as Monte Carlo simulations in model development. Rigorously back test and validate models to ensure their robustness, accuracy, and profitability in real-world betting scenarios. Drive and lead quantitative modelling initiatives, with a particular focus … findings and project outcomes clearly and persuasively to both technical and non-technical stakeholders, including senior leadership. Create basic reports and visualisations using tools such as Tableau to communicate model performance and insights. Required Skills and Experience: Proven experience as a Quantitative Analyst/Modeller with a track record of successfully leading and delivering impactful quantitative models in a … including a strong understanding of complex statistical distributions and Monte Carlo simulations. Highly proficient in Python for all modelling, analysis, and data manipulation work. Strong experience in back testing, validation, and performance evaluation of quantitative models. Solid understanding of the end-to-end model development and deployment lifecycle in a production environment. Experience in deriving markets for various More ❯

As data engineer, you will contribute to the development of a robust, validated and easily accessible data foundation instrumental in facilitating new analytical workflows and applications. About us Hexegic are a leading technical consultancy providing agile multi-disciplinary teams to More ❯