24 of 24 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 ...

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 ...

Connect to your Industry Deloitte’s AI for Sustainability and Infrastructure team sits within our Engineering, AI & Data offering area and is purpose built to help our client’s lead and win through the transition to a low‐carbon, digitally enabled, prosperous, equitable and resilient economy. We take work … transformative AI and Generative AI strategies aligned with business goals. Lead the design, development, and implementation of advanced AI pipelines, encompassing data acquisition, preprocessing, feature engineering, model development, evaluation, and secure deployment at scale. Oversee the development and operationalisation of state-of-the-art AI models, including large ...

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 ...

complex datasets to identify trends, patterns, and insights Design, develop, and deploy machine learning models and predictive solutions Perform data cleaning, preprocessing, and feature engineering Collaborate with business stakeholders to translate requirements into data solutions Build data visualizations, dashboards, and reports Evaluate and optimize model performance Communicate findings ...

Learning models (e.g., classification, regression, clustering, deep learning) to solve complex business problems. Data Wrangling & Analysis: Perform comprehensive Exploratory Data Analysis ( EDA ), data cleaning, feature engineering, and transformation on large, complex, and sometimes unstructured datasets. Coding & Automation: Write production-quality, highly efficient, and scalable code primarily in Python ...

Analyze structured and unstructured datasets to identify trends and insights Support the development and evaluation of machine learning models Perform data preprocessing, cleaning, and feature engineering Collaborate with data scientists and engineers to implement AI solutions Develop dashboards, reports, and visualizations to communicate findings Monitor model performance ...

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 ...

conversational analytics products • Develop LLM-driven systems including fine-tuning and prompt pipelines • Apply statistical modelling and quantitative analysis to financial datasets • Conduct feature engineering, model evaluation, and experimentation • Work with engineering teams to integrate models into AWS-based platforms • Support development of AI systems used ...

segmentation frameworks. Apply statistical modelling, machine learning and experimentation techniques to real‐world business problems. Use Python, R and SQL for data exploration, feature engineering, modelling and evaluation. Select appropriate techniques such as regression, tree‐based methods, clustering, time‐series analysis and experimentation. Ensure models are robust, interpretable … with clarity on business performance. Support market expansion and operational scaling by providing analytical expertise and performance optimization insights. Data Infrastructure & Compliance Partner with Engineering and Technology teams to ensure data pipelines and tooling to support scalable analytics and product development. Drive best practices in data ethics, privacy ...

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 ...

translate insights into business impact. Key Responsibilities Design and implement predictive and prescriptive models for MRO AI Solutions. Perform exploratory data analysis and feature engineering. Collaborate with Data Engineers to ensure data readiness for modeling. Communicate findings and recommendations to business stakeholders. Continuously improve models based on feedback ...

translate insights into business impact. Key Responsibilities Design and implement predictive and prescriptive models for MRO AI Solutions. Perform exploratory data analysis and feature engineering. Collaborate with Data Engineers to ensure data readiness for modeling. Communicate findings and recommendations to business stakeholders. Continuously improve models based on feedback ...

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. ...

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 ...

skills with experience querying large scale relational databases such as Azure SQL or PostgreSQL Experience working with high volume or time based data, including feature engineering Experience with simulation, discrete event simulation, or operations research techniques is highly desirable Ability to communicate complex analysis clearly to non-technical ...

Government (Grade 7 equivalent), or a highly regulated industry . Essential Experience: End-to-End ML: Proven experience delivering applied data science projects from feature engineering to deployment. Python Mastery: Expert-level Python skills with a focus on code structure, testing, and version control. Governance: A deep commitment ...

Python, deep learning, and MLOps, along with strong communication and problem-solving skills. The successful candidate will guide complex ML projects and support feature engineering while collaborating with talented teams to deliver scalable solutions that meet business needs. #J-18808-Ljbffr ...

into more advanced, scalable, and performant solutions while remaining grounded in business value. Key responsibilities Advise on advanced model design and selection Support sophisticated feature engineering strategies Guide teams through complex experimentation and optimisation Provide leadership on deep learning approaches Support modern model architectures and advanced techniques Help ...

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 ...

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 using ...