20 of 20 Permanent Feature Engineering Jobs in England

Work with large datasets to build predictive and analytical models • Collaborate with data scientists to transition models from research to scalable production systems ML Engineering and Deployment • Design and maintain machine learning pipelines and model deployment infrastructure • Implement automated model training, validation, and monitoring processes • Ensure models are scalable … reliable, and performant in production environments Data Engineering and Infrastructure • Work with data engineers to build efficient data pipelines and feature engineering processes • Optimise data workflows to support model development and experimentation • Contribute to the development of robust ML infrastructure and tooling Collaboration and Product Integration • Work ...

valuation models that drive real lending decisions worth hundreds of millions of pounds. You’ll work end-to-end - from exploratory analysis and feature engineering through to validated, production-ready models deployed in AWS. Critically, this is not a back-room role. You will regularly present model outputs … business strategy. Collaborate with senior leadership to quantify the business impact of AI initiatives and build the case for continued investment. Data Strategy & Engineering Conduct deep exploratory data analysis to identify new predictive features, data quality issues, and opportunities to improve model accuracy. Partner with data engineering ...

valuation models that drive real lending decisions worth hundreds of millions of pounds. You’ll work end-to-end - from exploratory analysis and feature engineering through to validated, production-ready models deployed in AWS. Critically, this is not a back-room role. You will regularly present model outputs … business strategy. Collaborate with senior leadership to quantify the business impact of AI initiatives and build the case for continued investment. Data Strategy & Engineering Conduct deep exploratory data analysis to identify new predictive features, data quality issues, and opportunities to improve model accuracy. Partner with data engineering ...

Team The Enterprise Solutions Technology team is dedicated to delivering next-generation, high-scale technology platforms through resilient architecture, data excellence, and engineering innovation. Our mission is to enhance our digital presence and improve customer engagement across various domains, including Lending, Corporate Actions, Tax, Regulatory & Compliance, Regulatory Reporting, Public … machine-learning systems across a complex, regulated financial-services estate. This is a strategy-led and hands-on applied data science and ML engineering role, responsible for defining the AI/ML roadmap for Enterprise Solutions while also building high-rigor analytical and predictive models for anomaly detection, variance ...

building the future of population health management. As a Senior Machine Learning Engineer, you will sit at the intersection of data science, platform engineering, and production systems. Your mission is to design and operate the foundations that allow ML, analytics, and agentic AI systems to move safely and reliably … from experimentation into regulated healthcare production environments on Google Cloud Platform. You will be the primary interface between Data Science and Platform Engineering, enabling rapid iteration while enforcing production and compliance standards. Responsibilities Design and implement Onsera's agentic AI platform – architect LLM/agent framework selection, standardized patterns ...

Location: Surrey, UK ( Office based ) Details: Full-Time | Truly competitive salary + Bonus + unrivalled benefits A global software company is evolving its core engineering platforms by embedding machine learning and applied AI into high-performance simulation and modelling tools used worldwide. This is a hands-on applied … role — focused on building and deploying ML solutions inside production-grade engineering systems, not isolated research or experimental prototypes. The position: You’ll design, build, and deploy machine learning models that directly enhance complex engineering software products. Expect a blend of ML engineering, software development, and computational ...

capable of reasoning and planning. Expertly handle LLM fine-tuning (PEFT, LoRA, QLoRA) and RAG pipelines. Data Orchestration: Build ETL/ELT pipelines and feature engineering workflows to integrate structured and unstructured data into centralized platforms. End-to-End MLOps: Own the lifecycle—from CI/CD automation … every system is trustworthy, fair, and explainable, implementing quantifiable metrics for bias detection and regulatory compliance. Technical Toolkit Models: LLMs, Generative AI, Agentic workflows. Engineering: PEFT, Vector Databases (Pinecone/Milvus/Weaviate), Prompt Engineering. Ops: Docker, Kubernetes, CI/CD, Experiment Tracking (MLflow/W&B). Data ...

effectively, directly influencing the way millions of people engage with their products every day. The Opportunity This is a unique chance to combine data engineering with machine learning in a high-impact environment. You'll work closely with analysts, data engineers and stakeholders, ensuring models are reliable, scalable … decision-making and user experiences for a vast audience. Your day-to-day will include: Building and maintaining end-to-end data pipelines and feature engineering workflows. Deploying and monitoring ML models in production using tools such as MLflow, Vertex AI, or Azure ML. Driving best practices ...

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 ...

data architecture initiatives on Snowflake, Databricks and cloud‐native services, ensuring scalability, interoperability, security and regulatory compliance. You will collaborate with Data Science, ML Engineering and Enterprise Data teams to deliver high‐quality, governed and reusable data products for a wide range of business and regulatory needs. Your responsibilities … Architect and govern end‐to‐end data platforms supporting Data Science and ML lifecycles (ingestion, feature engineering, training, inference, monitoring). Design and manage multi‐cloud data estates across AWS, Azure and/or GCP, ensuring scalability, resilience and cost efficiency. Define architecture patterns and best practices ...

Senior Data Scientist to take a leading role across a portfolio of technically demanding projects. You’ll operate end-to-end - shaping problem definitions, engineering data pipelines, developing advanced models, and ensuring outputs are robust, explainable and deployable in real-world settings. This is a hands-on role … impact. Key Responsibilities Translating complex, ambiguous problem statements into clear, actionable data science solutions. Owning the full data science lifecycle, from data ingestion and feature engineering through to modelling, evaluation and deployment. Developing statistical, machine learning and deep learning models to support high-impact, real-world decision making. ...

including Microsoft Fabric, Azure AI and ML tooling Key skills Strong experience with Microsoft Fabric (Data Factory, Lakehouse, OneLake, Pipelines, SQL Warehouse) Solid data engineering background using SQL, Python, PySpark, Delta Lake E xperience building and optimising end-to-end data pipelines CI/CD experience for analytics … hybrid platforms to a modern Fabric setup Supporting BI teams with semantic models, datasets and performance optimisation Enabling ML and MLOps workflows, from feature engineering to deployment Working closely with data scientists, analysts, IT and the wider business Embedding data quality, governance and best-practice standards Providing technical ...

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 ...

emphasis is on refinement, optimisation and continuous improvement rather than building from scratch. You will be responsible for the intellectual core of the models: Feature engineering across financial, behavioural and transactional data Algorithm selection and tuning (logistic regression, gradient boosting, ensemble methods) Model validation, performance optimisation and ongoing … recalibration Ensuring models remain robust in changing economic conditions You will not be responsible for infrastructure, pipelines or deployment. A dedicated engineering team manages AWS and production environments, allowing you to focus on modelling and analytics. This is a highly visible role with direct exposure to senior stakeholders. ...

viable and worth scaling. 🧩 The Role You will take early-stage concepts and independently build end-to-end proofs of concept (PoCs), combining data engineering, machine learning, and lightweight application development. Where a PoC shows promise, you will extend it into a functional prototype aligned to real business needs. … Responsibilities Build PoCs from loosely defined ideas within 2–3 days Design and implement end-to-end experimental solutions (data → model → output) Combine data engineering, analytics, and ML to test feasibility quickly Develop lightweight prototypes (APIs, notebooks, dashboards, simple UIs) Translate business ideas into technical approaches independently Iterate rapidly ...

Must-haves 3+ years experience in data science Strong Python and SQL skills Experience building and evaluating predictive models Understanding of data modelling and feature engineering Ability to translate data into clear commercial insight and recommendations Confident communicator - comfortable working directly with clients Consultancy or client-facing experience ...

ownership and production-grade model deployment. The Role As Senior Data Scientist, you will: Own end-to-end ML solutions -from problem framing and feature engineering to deployment, monitoring, and governance Translate business objectives into modelling strategies aligned to risk appetite and operational constraints Build and deploy models … using Python, SQL, and AWS (SageMaker or equivalent) Partner closely with Engineering, Data, and Risk/Financial Crime teams to ensure robust, production-ready solutions Establish monitoring frameworks for performance, drift, and retraining Drive clear documentation, traceability, and governance appropriate for a regulated environment This role requires someone ...

integrated applications across on-prem and cloud. Secure MLOps/ML platforms: architect identity, secrets management, network segmentation, and least-privilege access for feature stores, model registries, orchestration, and deployment pipelines. Data protection by design: establish controls for sensitive data ingestion, anonymization/pseudonymization, encryption (at rest/… identity, secrets management, key management (KMS/HSM), network segmentation, and policy-as-code. Strong knowledge of AI/ML workflows: data ingestion/feature engineering, model training/inference, MLOps tooling (model registry, orchestrators, serving). Practical experience with adversarial ML concepts and defenses; familiarity with model ...

radar, LiDAR, RF, imagery, etc.) Building machine learning models for classification, regression, and tracking Applying statistical techniques to quantify uncertainty and improve predictions Performing feature engineering and dimensionality reduction on spatial data Building tools to visualise and validate model outputs Working closely with engineers to deploy models into … etc.) Solid understanding of machine learning and statistical modelling Experience working with complex or large-scale datasets Ability to work in a cross-functional engineering environment Nice to Have Experience with sensor data (radar, LiDAR, satellite, RF, etc.) Knowledge of tracking or filtering techniques (e.g. Kalman Filters, Bayesian methods ...

experimentation strategy for the product area — designing A/B tests and multi-armed bandit experiments that generate genuine learning about AI feature performance, not just validate existing assumptions Drive the feedback loop strategy — defining how member behaviour is captured, modelled, and fed back into AI ranking systems … context, using results to change direction rather than confirm instinct Technically credible AI and ML fluency — able to read a model card, challenge a feature engineering decision, write testable acceptance criteria for ML features, and explain confidence intervals to non-technical stakeholders, without overstepping into engineering decisions ...