18 of 18 Feature Engineering Jobs in London

building, and deploying scalable machine learning solutions to enable advanced analytics, automation, and decision-making across complex government systems. This is a hands-on engineering role, working closely with data scientists, analysts, architects, and product teams to productionise models, build data pipelines, and embed ML capabilities into live services. … Synapse) Experience in AWS environments is beneficial Experience with MLOps practices and tools (CI/CD pipelines, model monitoring, versioning) Strong understanding of data engineering concepts (ETL pipelines, data lakes, feature engineering) Experience working with large-scale structured and unstructured datasets Knowledge of AI ethics, bias mitigation ...

graph databases and knowledge graphs, to generate deep insights into financial markets and enhance Marquee’s platform engagement. We collaborate closely with Sales, Trading, Engineering, Product, Design, and other areas of the Global Markets Division, and directly with clients. Our global team comprises experts in financial markets, product structuring …/PyTorch), Jupyter, Trino, SQL, and gain exposure to graph database technologies. RESPONSIBILITIES AND QUALIFICATIONS Responsibilities: Design, build, and maintain robust data pipelines for feature engineering and model training, ensuring data quality, scalability, and explainability. Develop, train, and deploy state‐of‐the‐art machine learning models, with ...

Python and SQL skills Experience developing and deploying machine learning models into production environments Excellent understanding of statistical modelling, experimentation, and model evaluation Strong feature engineering and exploratory data analysis capabilities Experience communicating technical findings and insights to non-technical stakeholders A degree qualification (BSc, MSc, PhD etc. … innovation The Senior Data Scientist role will involve: Developing and deploying machine learning models to solve real-world business challenges Performing exploratory data analysis, feature engineering, model development, and evaluation Building forecasting, classification, segmentation, and predictive modelling solutions Designing and executing statistical analyses and experiments to support strategic ...

years to come. AI at the Core: This is a true AI/Data‐driven product. ML isn’t a nice‐to‐have feature – it's the product. Your infrastructure directly enables business value. Truly Cross‐functional: The Global:IQ team is a tight collaboration between technical and commercial … areas. Measures of Success In the first few months, you would have: Defined a clear operating model between Data Engineering/MLOps and teams responsible for model development. Onboarded key 1st and 3rd party datasets following existing ingestion patterns/standards. Delivered an initial end‐to‐end MLOps path ...

technical leadership across the full lifecycle of machine learning systems powering a new matching platform. This role is accountable for defining ML architecture, establishing engineering standards, driving MLOps maturity, and ensuring that our models are scalable, secure, explainable, and governed to enterprise‐grade standards. You will contribute … compliance framework. This is a highly hands‐on, highly technical, principal‐level role that combines architectural vision with deep practical expertise in ML engineering and AWS-native MLOps. Key Responsibilities Technical Leadership & Architecture Define the end‐to‐end ML architecture for the matching platform, including data pipelines, model training ...

will be responsible for designing, building, and governing predictive models that support data-driven products and strategic decision-making. You will work closely with engineering and product teams to transform raw data into robust, auditable, and commercially valuable solutions. Key Responsibilities Partner with data engineering teams to extract … predictive models for use within regulated financial services and credit risk environments. Own the full data science life cycle, including: Exploratory Data Analysis (EDA) Feature engineering and selection Model development and validation Calibration and performance monitoring Build forecasting models, simulations, and stress-testing frameworks to support risk ...

Join our global Quantitative Trading & Research (QTR) team, where you’ll apply your expertise in Derivatives Modelling, Financial Engineering, Data Science, and Quantitative Development. As part of JP Morgan’s leading QTR Group, you’ll innovate with unique analytics and mathematical models, enhancing business practices through automation. We develop … limitations, and model performance. Build and productionize analytics that advance intraday/EoD automation (services, APIs, pipelines) with clear SLOs/SLA, observability, reliability engineering practices, and tight integration into trading/risk platforms. Provide technical leadership and mentorship; conduct code/method reviews, establish research engineering best ...

logistic regression, gradient boosting, random forests, clustering, NLP Proficient in Python and/or R, with strong experience in data quality, model development and feature engineering Strong command of SQL and familiarity with data engineering environments such as Databricks or similar Skilled in data visualisation and storytelling ...

audit requirements. Experience with GDPR and AI governance frameworks. Key responsibilities Build and deploy predictive and credit risk models. Lead exploratory data analysis, feature engineering, model validation and monitoring. Develop forecasting, simulation and stress-testing models. Work with data engineering teams to create modelling-ready datasets using ...

explainability, and performance evaluation Requirements Strong experience as a Senior Quant Data Scientist Deep understanding of: Regression, classification, and ranking models Time-series analysis & feature engineering Hands-on experience with: Python (pandas, NumPy, scikit-learn, etc.) Gradient boosting frameworks (XGBoost/LightGBM/CatBoost) Experience working with LLMs ...

limitations, and model performance. Build and productionize analytics that advance intraday/EoD automation (services, APIs, pipelines) with clear SLOs/SLA, observability, reliability engineering practices, and tight integration into trading/risk platforms. Provide technical leadership and mentorship; conduct code/method reviews, establish research engineering best … resourcing via data‐driven analysis, scenario studies, and clear articulation of trade‐offs. Lead development of ML/AI solutions end‐to‐end (feature engineering, model training/validation, MLOps, monitoring and drift management) with rigorous controls, documented governance, and demonstrable business value. Uphold standards for documentation, reproducibility ...

This is a senior individual contributor role with strong influence through mentorship, collaboration, and technical leadership. You will work closely with partners across product, engineering, and the business to design and deliver data‐driven solutions.We welcome candidates from a range of backgrounds and experiences who are excited to apply … Development** Lead by example through hands-on coding and model development. Design, prototype, and validate innovative solutions for high-impact problems. Partner with engineering teams to deploy models into production environments.* **Domain Expertise** Leverage deep knowledge of aviation, travel, or related industries to inform modeling approaches and deliver customer ...

problem definition through to deployment in live environments.* Design, build, and deploy machine learning models within Pega-based decisioning platforms.* Lead model development across feature engineering, training, validation, and ongoing performance monitoring.* Work closely with application and logic developers to embed models into operational decision workflows.* Develop statistical ...

topics, including: Fundamentals of ML and AI (including Machine Learning Lifecycle, Machine Learning Mathematical Foundations); Solving Business Problems with Machine Learning; Data Preparation and Feature Engineering; Planning and preparing ML/AI solution; Data Ethics and Responsible AI; Model Engineering and Training; Model Evaluation and Deployment; Monitoring ...

advanced ranking models for Depop’s entire app, covering personalised recommendations, search results and other surfaces. Collaborate closely with cross-functional partners (product, engineering, data) to define problems, translate them into scalable solutions, and deliver measurable business outcomes. Lead the end-to-end lifecycle of ML projects: from ideation … data acquisition, feature engineering, training, and evaluation to deployment and ongoing iteration. Drive innovation in ranking by researching and integrating emerging ML techniques, frameworks, and tooling, while contributing technical expertise to long-term product and data strategy. Mentor, coach, and set technical direction within the Ranking ML team ...

build scalable forecasting systems that continuously generate, deploy and improve predictive models. You'll work with large-scale data, applying machine learning, experimentation and feature engineering to improve prediction accuracy and drive better customer outcomes. You'll also help shape product strategy, establish data science best practices ...

work. The Role and Deliverables Design, develop, and enhance statistical and predictive models using SAS across the full model development lifecycle. Build modelling datasets, feature engineering processes, and variable transformations. Develop model logic, segmentation rules, scoring code, and scenario analysis. Carry out model validation, back-testing, sensitivity analysis ...

will be responsible for designing, building, and governing predictive models that support data-driven products and strategic decision-making. You will work closely with engineering and product teams to transform raw data into robust, auditable, and commercially valuable solutions. Skills & Experience Technical Expertise Strong proficiency in Python (Pandas, NumPy … Snowflake or similar cloud-based data warehouse platforms. Experience using Git and reproducible development practices. Strong understanding of data pipelines, data quality, and analytics engineering principles. Credit Modelling & Predictive Analytics Proven experience conducting rigorous exploratory data analysis and feature engineering. Experience building predictive models in regulated financial services ...