11 of 11 Feature Engineering Jobs in Central London

Responsibilities Lead the design, development, and maintenance of credit risk and affordability models using bureau, open banking, and behavioural data Own the full model lifecycle from data sourcing and feature engineering to validation, deployment, and monitoring Design and run A/B and champion/challenger tests to improve performance across approval rates, losses, and customer experience Analyse … credit performance data to deliver insights that guide strategic decisions Mentor and support junior analysts/data scientists as the team expands Collaborate with data engineering to deploy models into production Work closely with stakeholders to define goals, communicate findings, and translate model outputs into business value About You You are an analytical thinker with a passion for using … data to drive credit decisioning. You bring hands-on experience building machine learning models for consumer credit and understand the nuances of data preparation, feature selection, and model validation in high-stakes environments. 5–7 years of experience in a credit-related data science or decision science role Proficiency in Python and experience with libraries such as scikit-learn More ❯

deploy predictive models using behavioural and real-world data Translate complex datasets into actionable insights that improve healthcare outcomes Contribute to the full ML lifecycle, from data wrangling and feature engineering to model deployment and monitoring Collaborate with engineering and product teams to ensure models are integrated into production systems via scalable pipelines Champion best practices in More ❯

science solutions that deliver commercial value Lead the design, development, and deployment of predictive and optimisation models across multiple industries Own the end-to-end ML pipeline: data exploration, feature engineering, modelling, evaluation, and deployment Collaborate with data engineers and MLOps professionals to ensure scalable, production-grade solutions Conduct rigorous A/B testing and performance measurement to … engineers, product leads, and client stakeholders Experience with A/B testing and causal inference methodologies Bachelor’s or Master’s degree in a quantitative field (e.g., Mathematics, Physics, Engineering, Computer Science) Excellent communication skills and a collaborative mindset Example Projects You Might Work On Optimisation models to increase manufacturing throughput Predictive models for asset maintenance and downtime reduction More ❯

Full-time | Hybrid Join a tech consultancy at the forefront of applied AI—helping global brands design and deploy intelligent systems that solve real business problems. They combine deep engineering know-how with domain expertise to build the data infrastructure that powers machine learning and generative AI at scale. 🔍 About the Role As a Lead Data Engineer , you’ll … platforms using tools like Databricks , Airflow , Snowflake , and Spark Collaborate with AI/ML teams to align data processing with model requirements Develop ETL/ELT workflows to support feature engineering, model training, and inference Optimise data workflows for scalability, reliability, and cost-efficiency Ensure security, compliance, and data governance standards (e.g. GDPR , RBAC) Mentor junior engineers and More ❯

drive innovation into the product development process. You will interpret your client’s objectives, desires, and preferences to help the wider technical team understand the opportunities and apply data engineering responsibilities consistently. You will be working on an interesting range of projects that deliver to National Security customers and as such, you will have to hold the highest level … of UK Security Vetting (DV), upon application. Key Responsibilities: You will blend Data Engineering and Data Science, and will have experience that might cover a number of the tasks listed below: Data Engineering tasks Manage the implementation and development of integrations between the data warehouse and other systems. Create deployable data pipelines that are tested and robust using … Research, analyse and apply data sets using a variety of statistical and machine learning techniques. Support the analytical needs of the technical team inclusive of cleansing, mapping, statistical inferences, feature engineering and the bespoke data visualisation methods required by each project. Review the execution of software solutions and how these perform for the business and your clients, establishing More ❯

model building, evaluation, and visualization. Proficient in Python programming and SQL , with experience in production-level code and data analysis libraries. Familiar with ML Ops , model development workflows, and feature engineering techniques. Capable of manipulating data and developing models accessible for business use, with experience in Azure AI Search . Adept with software development methodologies, code versioning (e.g. More ❯

markets. Responsibilities: Lead the development and maintenance of credit risk and affordability models using bureau, open banking, and alternative behavioural data. Own end-to-end model lifecycle: data sourcing, feature engineering, model development, validation, and monitoring. Design and execute champion/challenger tests and A/B experiments to continuously improve approval rates, loss rates, and customer experience … performance data to generate actionable insight and support strategic decisions Mentor and develop a small team of analysts/data scientists as the team scales Work closely with Data Engineering to deploy models into production pipelines. Collaborate with stakeholders to define modelling goals and interpret model outcomes in a business context. Requirements: MSc or PhD Degree in Computer Science More ❯

commercial uplift—delivering solutions that are accurate, explainable, and production-ready. Key Responsibilities Build machine learning models for forecasting, propensity scoring, and segmentation Own workflows from data wrangling and feature engineering through to deployment and monitoring Operationalise models using MLOps tools in a cloud-native environment Architect datasets by merging structured and semi-structured data Work closely with More ❯

in a technically rich environment. What You’ll Do Develop and deploy machine learning models including classification, regression, and clustering Work across the full model lifecycle: data prep, EDA, feature engineering, training, evaluation, and scaling Build predictive and propensity models for underwriting and claims Apply NLP and OCR for document classification and summarisation Collaborate with clients and cross More ❯

that underpin lending decisions and risk management strategies. Key Responsibilities: Develop, implement, and maintain credit risk scorecards, applying advanced statistical and machine learning techniques. Utilize Python for data extraction, feature engineering, model development, validation, and ongoing monitoring. Collaborate with risk management, analytics, and IT teams to ensure model robustness, compliance with regulatory standards, and effective integration into production More ❯

success criteria. Support and mentor junior team members, fostering a collaborative environment. Independently manage client relationships and project expectations. Develop and deploy ML models, applying innovative approaches beyond basic feature engineering. Use Python, SQL, and Tableau for reporting and analysis within an AWS environment. Work closely with Data Engineering, Sales, and Market Research teams across the UK, US … meaningful insights to drive business decisions. Strong communication skills, with experience in managing client relationships. A degree in a relevant field such as Mathematics, Statistics, Economics, Psychology, Sociology, Physics, Engineering, or Computer Science (Master’s or PhD is a plus). Interest or experience in TV viewing data, media, or marketing analytics is beneficial but not essential. If this More ❯