76 to 100 of 131 Feature Engineering Jobs in England

Data Science team is building a layered reliability platform that shifts CoreWeave from reactive troubleshooting to proactive reliability engineering. The platform spans telemetry ingestion, feature engineering, anomaly detection, failure prediction, distributed straggler detection, and agentic root cause analysis. As a forward deployed function, we partner closely with Fleet … intersection of data science and production systems, deploying advanced statistical models and machine learning methodologies directly within operational environments. You will collaborate closely with engineering and infrastructure teams to optimize GPU utilization, workload scheduling, and system efficiency in real time. You will design experiments, analyze large-scale system telemetry ...

machine learning platforms. Data governance and metadata tooling to improve transparency, trust, and reuse across the organisation. Your Experience 5+ years experience in data engineering, building and maintaining scalable data platforms. Strong SQL and data modelling skills, with experience designing analytical datasets. Experience with modern data stack tools (e.g. … e.g. Cube.js) or metrics‐layer design. Exposure to data governance and metadata tools (e.g. OpenMetadata, Hive Metastore). Experience supporting machine learning workflows and feature engineering pipelines. Why This Role Matters This platform is central to how we enable trusted, scalable, and reusable data across sportsbook. It provides ...

machine learning solutions from exploration through deployment and ongoing performance management. Strong Python and SQL skills, including experience building maintainable data science workflows, feature engineering pipelines, experimentation frameworks, and model evaluation code for production settings. Working knowledge of GCP-based data and ML environments, preferably including BigQuery, Vertex ...

Development: Design, train, and optimise machine learning models focused on user personalisation, encompassing recommendation engines, ranking algorithms, user segmentation, and content analysis Data Pipeline Engineering: Construct and maintain robust and scalable data pipelines for feature engineering and model training utilising both structured and unstructured large-scale datasets ...

models (not just consuming APIs) A solid understanding of model evaluation and validation, overfitting and bias/variance trade‐offs, and data quality and feature engineering Proficiency in Python and ML frameworks such as PyTorch or TensorFlow Experience building and maintaining ML pipelines in production Familiarity with ...

models (not just consuming APIs) • A solid understanding of model evaluation and validation, overfitting and bias/variance trade-offs, and data quality and feature engineering • Proficiency in Python and ML frameworks such as PyTorch or TensorFlow • Experience building and maintaining ML pipelines in production • Familiarity with ...

Bring Hands‐on experience in data science or applied machine learning. Strong understanding of Generative AI, LLMs, and RAG principles. Experience with data mapping, feature engineering, and end‐to‐end ML lifecycle management. Proficiency across Microsoft's Azure AI and data ecosystem, with an eagerness to adopt Fabric ...

financial services (ideally Anti-Financial Crime/Screening) Experience building Natural Language Processing (NLP)/Machine Learning (ML)/Artificial Intelligence (AI) pipelines, including feature engineering, model training, evaluation and monitoring Experience with hyperparameter tuning at scale and experimentation frameworks Strong technical experience with SQL and Python; significant ...

forecasting, behavioural signals, and prediction, ensuring they are accurate, explainable, resilient, and regulator-ready. Own the full model lifecycle from problem framing, data exploration, feature engineering, back-testing, validation, deployment, monitoring, tuning, and incident response when models degrade or fail in production. Partner closely with engineering, data … Strong hands‐on technical skills, with the ability to work directly with data, code, ML frameworks, and complex datasets, while partnering effectively with software engineering and platform teams. Experience building scalable, reliable, and explainable AI/ML solutions that meet operational, governance, and regulatory requirements and deliver measurable business ...

Responsibilities: Develop and implement machine learning and AI models Develop test and evaluation protocols on ML/AI models Assist in data pre-processing, feature engineering, and exploratory data analysis Contribute to building scalable and maintainable AI software systems Debug, test, and improve existing models and codebases Document … soon as practical. About Us The National Physical Laboratory (NPL) is a world-leading centre of excellence that provides cutting-edge measurement science, engineering and technology to underpin prosperity and quality of life in the UK. NPL and DSIT have strong commitments to diversity and equality of opportunity ...

Azure, or Google Cloud. ✔️ Collaborate with cross-functional teams to define AI project goals. ✔️ Translate complex data insights into actionable business recommendations. ✔️ Perform feature engineering and data preprocessing. ✔️ Maintain clear documentation of models, datasets, and experiments. ✔️ Develop APIs for model integration with front-end systems. ✔️ Conduct peer code ...

Solid Python programming skills and understanding of machine learning fundamentals. Growing familiarity with AI technologies, particularly LLMs and Azure OpenAI. Experience with data preparation, feature engineering, and model evaluation. Good communication skills and ability to explain technical concepts to non-technical audiences. A commitment to Movar's IDEAL ...

economic return optimization. You’ll develop calibration frameworks, ensure compliance with regulatory and fairness standards, and explore novel methodologies — including LLMs for explainability and feature engineering. Collaborating with cross-functional teams, you’ll translate modeling insights into strategic credit policies and business value, while mentoring junior team members ...

move more freely between more people , making the online shopping experience seamless and accessible to everyone. THE TEAM ~110 people , more than half in engineering, product and data 45+ advanced degrees across computer science, mathematics and operations research Thousands of data points captured, calculated, analysed and predicted for every … Work with team members across the business to understand their data needs and deliver solutions Shape our practices — Help define and evolve our analytics engineering processes and standards as we scale What We're Looking For 5+ years of experience as an analytics engineer, data engineer, data analyst ...

BigQuery to build and support high-performance models at scale. What you'll do Contribute to the full model lifecycle , from initial discovery and feature engineering through to production, experimentation, and continuous monitoring. Design and refine real-time ML systems that reduce false positives and ensure a smooth ...

Data Scientist to drive production‐ready ML solutions that impact business outcomes. You will translate complex data challenges into scalable models, spanning data extraction, feature engineering, experimentation, and deployment. You will work closely with Product, Engineering and Marketing to optimize campaigns, improve user experiences, and inform strategic … decisions, while maintaining engineering rigor and robust ML ops practices. #J-18808-Ljbffr ...

Monitoring (TM)solution, you will own the tactical execution of features in an enterprise-grade AML/CLMplatform.You’llwork closely with cross-functional teams (engineering,customers, productmanagersandclient solutions) to deliver mission-critical capabilities:detectionengines,AI models,AI agents,tuning & calibration,alertand case managementUX, audit and reporting. You will translate … evolving threatsusing ground-breaking technology. Key Responsibilities Decompose high-level features/requirementsinto user stories, acceptance criteria, and clear backlog prioritization. Collaborate with engineering, data science, andsolutions teamsto deliver high-quality TM features (real-time and batch pipelines,detection analytics,alert scoring,alert/case creation, feedback loops). ...

Build and maintain end-to-end ML pipelines covering data preparation, experimentation, training, evaluation, deployment, monitoring, and optimisation. Develop large-scale data processing and feature engineering solutions using technologies such as PySpark and Databricks. Create multimodal machine learning models that combine image, text, metadata, and behavioural data … productionise innovative machine learning solutions. YOUR KEY SKILLS Significant experience designing, deploying, and supporting machine learning systems in production environments. Strong Python software engineering expertise, including testing, code quality, and maintainable application development. Proven experience working with large-scale data processing technologies such as PySpark and Databricks. Strong background ...

machine learning models for user personalisation, including recommendation engines, ranking algorithms, user segmentation, and content analysis Build and maintain robust, scalable data pipelines for feature engineering and model training using structured and unstructured large-scale datasets Deploy and supervise ML models in production environments to ensure high availability ...

extraction/resolution Knowledge Graphs; building and inference, graphRAG Recommendation systems and ranking: personalisation, learning-to-rank, feed ranking, user modelling, etc. Tabular ML: feature engineering, structured data modelling (GBDTs, etc.) Statistical & Predictive Modelling: supervised ML, anomaly detection, temporal modelling, etc. Deep understanding of the GenAI landscape ...

scale retrieval. Mentor, coach, and set technical direction within the Recommendations team, helping others grow and innovate. 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 recommendation systems by researching and integrating emerging ML techniques, frameworks, and tooling, while contributing technical expertise to long-term product and data strategy. Act as a thought leader in the recommendations space ...

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 ...

Skills & Experience Required – Technical Either MSc in Statistics or a related field (e.g., Data Science or Mathematics), or another field (such as Computer Science, Engineering, Finance, etc) with some experience in statistics and 3+ years of work experience in a relevant role (e.g. sports quantitative analyst for a betting … exchange); or a PhD or equivalent in Statistics or a related field (e.g., Data Science or Mathematics), or another field (such as Computer Science, Engineering, Finance, etc) with some experience in statistics Extensive experience of probabilistic and statistical modelling Model diagnostics: Ability to evaluate predictive model performance using appropriate ...

typically lead a team or the solution delivery effort, demonstrating technical excellence through leading by example. You could be providing technical support, leading an engineering team or working across multiple teams as a subject matter expert who is critical to the success of a large programme of work. Your … deployments. Shape team behaviour for writing specifications and acceptance criteria, estimating stories, sprint planning and documentation. Actively define and evolve PA’s data engineering standards and practices, ensuring we maintain a shared, modern and robust approach. Lead and influence technical discussions with client stakeholders to achieve the collective ...

Data Scientist to join our Data Practice, delivering enterprise-scale data platforms, analytics and AI-enabled decision support. You will contribute across model development, feature engineering, training and deployment, collaborating with engineers and consultants. Ideal candidates will have 3–5 years in data science, strong Python ...