1 to 25 of 30 Permanent Feature Engineering Jobs in England

Consulting Firms. Our Solutions & AI Labs (SAIL) practice is looking for an experienced Senior Manager to lead consultancy engagements and grow our AI & Solutions Engineering capability. The Forward Deployed AI & Solutions Engineer role will lead the delivery of AI-enabled solutions embedded in our clients’ teams, combining deep technical … solutions directly embedded into business processes. We support clients across a number of industries and offer deep expertise in AI/ML, Cloud, Platform Engineering, and Managed Solutions. What you will be doing As a Senior Manager in SAIL, you will own and lead consultancy engagements ...

Principal, AI/ML Engineer leads the design, delivery and continuous improvement of AI and ML solutions within their area of responsibility, aligning engineering activities to the Bank’s broader AI strategy and long‐term objectives. The role combines strong technical leadership with a clear focus on delivery outcomes … operates with a clear understanding of the wider technology landscape, contributing to strategic direction while taking accountability for translating this into practical, high‐quality engineering outcomes across squads. AI/ML Engineering Direction Leads the definition and execution of AI/ML engineering direction across ...

Experience designing and analysing A/B tests, defining success metrics, understanding selection bias, and evaluating ranking systems using both online and offline methodologies. Feature Engineering & Applied Machine Learning : Strong background in feature engineering, tabular machine learning, calibrated scoring systems, and production use of models such … Entity‐Aware Ranking : Ability to incorporate relationships between companies, sponsors, lenders, law firms, instruments, documents, and events into ranking and recommendation decisions. Production ML Engineering : Experience building feature pipelines, online and batch inference systems, low‐latency ranking services, experimentation platforms, and monitoring for production machine learning systems. Strong ...

Lead, Applied Scientist and Research Engineer positions—for upcoming roles across applied machine learning, generative AI, MLOps and model productionisation. These are hands-on engineering roles where you'll build, train, ship and operate the models that drive real product behaviour and real business outcomes. You'll work across … lifecycle—from problem framing and data preparation through to model development, evaluation, deployment, monitoring and iteration. The role suits someone who pairs solid software engineering foundations with genuine ML rigour and a healthy scepticism about what models can and can't be trusted to do. Key Responsibilities Partner 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 ...

generating, deploying and continuously improving bespoke machine learning models for every customer. You'll work with large-scale behavioural event data, applying machine learning, feature engineering and experimentation to improve prediction accuracy and drive measurable customer outcomes. Working alongside highly experienced founders and recognised experts in machine learning … business. Skills & Experience Strong commercial experience building machine learning models in Python Excellent understanding of predictive modelling, classification or propensity modelling Experience with feature engineering, model evaluation and experimentation Comfortable working across the full machine learning lifecycle Experience working with cloud platforms and modern ML tooling Curious ...

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 ...

Work With Building and deploying AI and Machine Learning solutions Designing and implementing end-to-end AI and ML pipelines, from data acquisition and feature engineering through to deployment and optimisation Developing scalable ML pipelines and engineering frameworks AI model training, evaluation, optimisation and deployment Cloud-native … stakeholders to understand business challenges and translate requirements into AI solutions Working closely with technical and non-technical teams throughout programme delivery Cross-functional engineering, architecture and data teams Agile software development and delivery methodologies Exploring and adopting emerging AI technologies and approaches to drive innovation and business value ...

full delivery lifecycle, from early ideas through to production systems that create measurable impact for our customers and the business. As MLOps Engineering Manager, you will help shape how we build, deploy and operate machine learning products at scale, working closely with ML Engineers, Data Engineers, Software Engineers, Data … operation of machine learning products, ensuring models and AI systems are production‐ready, observable and able to support Trainline’s growth. Partner closely with engineering, data science, product and data teams to bring strong engineering standards into machine learning delivery, while recognising the specific challenges of data ...

such as GANs and VAEs. Proven experience applying these techniques to real-world tasks (text, code, image, multimodal generation) and familiarity with advanced prompt engineering, structured outputs, function/tool calling, and orchestration frameworks like LangChain, LangGraph, LlamaIndex, and Semantic Kernel. Hands‐on exposure to API-based LLM providers … project management skills, overseeing timelines, milestones, and deliverables; coordinating internal teams and clients to ensure project success. Data Understanding : Foundational grasp of data preprocessing, feature engineering, and data quality assurance processes to support AI model requirements. Responsible AI & Governance : Awareness of AI governance, safety, and compliance considerations—data ...

value creation initiatives to be backed by reliable, real-time data. When companies partner with JMAN, we combine our data science and data engineering expertise with our deep commercial understanding to deliver tangible, high-value outcomes at pace. Founded in 2010, JMAN has a global footprint with offices … Data Science or applied machine learning, ideally in consulting, high‐growth, or commercial environments. Strong applied machine learning expertise, including: Model selection and development Feature engineering Evaluation and validation Deployment into production environments Demonstrated experience taking models beyond experimentation into operational, business‐critical systems. Strong background in statistics ...

Bedrock. What you’ll do Partner with stakeholders across the business to explore high‐impact opportunities. Own the full lifecycle: problem framing, data discovery, feature engineering, modelling, evaluation, deployment, monitoring, and iteration. Build and productionize LLM features where appropriate (retrieval‐augmented generation, evaluation, safety guardrails, cost/latency … Contribute to DS/ML standards: experimentation, model governance, documentation, and reproducibility. Mentor junior scientists, work with external contractors and collaborate closely with data engineering on pipelines and data quality. What you’ll bring A proven track record delivering projects in a Data Science or AI. Experience deploying models ...

play a key role in designing, developing and deploying AI and Machine Learning solutions, working across the full ML lifecycle from data preparation and feature engineering through to model deployment and optimisation. Working with Python, SQL and leading ML frameworks, you'll build scalable pipelines, develop and evaluate ...

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 ...

Experience designing and operating modern data platforms Experience developing or enabling AI and machine learning capabilities using business data Strong understanding of data preparation, feature engineering and model evaluation Experience with Python-based data and ML tooling, e.g. Pandas, NumPy and scikit-learn Experience with ML tooling within ...

platform design. Ensure architectures meet security, privacy, and regulatory requirements, including GDPR and data residency considerations. Review solution designs, provide architectural assurance, and support engineering teams through design authority and peer review. Design data architectures that enable AI adoption — ensuring data is discoverable, well-governed, and AI-ready … machine learning, GenAI, and agentic use cases across the business. Partner with AI/ML teams to define patterns for feature engineering, model data pipelines, vector search, and safe exposure of enterprise data to AI systems. Evaluate emerging technologies and make pragmatic build/buy/adopt recommendations ...

client engagements to assess and remediate financial crime risk across payments and banking environments. Drive data diagnostics and analytical assessments including data profiling, feature engineering, exploratory analysis, and performance benchmarking. Evaluate technology solutions for AML monitoring, customer risk scoring, and customer screening, and support vendor selection and integration … processes. Collaborate with client data engineering teams to advise on data pipelines, schema design, and pipeline infrastructure needed for analytic and monitoring use cases. Translate complex technical concepts into clear, executive‐level recommendations and actionable roadmaps. Business Development and Lead Generation Maintain and develop a personal profile ...

communicating uncertainty, assumptions, and limitations.* Support experimentation in pricing and risk assessment approaches while maintaining defensibility and alignment with governance expectations.* Collaborate with data engineering and application developer colleagues to support deployment, monitoring, and scaling where production use is appropriate.* Contribute to the development of a consistent, cross-line … Experience Specification:**Hard Skills* Strong applied data science capability (using Python/R to apply statistical modelling and machine learning techniques) including model design, feature engineering, validation, version control and documentation, with key focus on transparency and explainability throughout.* Sound statistical and analytical foundations (probability theory, GLMs, time ...

play a key role in shaping how data are harnessed to monitor and supervise firms and markets Qualifications Demonstrable and advanced data science and engineering experience within a financial markets context Able to design efficient SQL (Redshift) and Python code for complex ETL pipelines and feature engineering ...

using Python, TensorFlow, PyTorch Work on supervised, unsupervised, and reinforcement learning problems Clean and process large datasets to extract insights and train algorithms Perform feature engineering to improve model accuracy Design ML pipelines and automate model deployment Collaborate with software engineers to integrate models into applications Use MLOps ...

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 ...

complex real-world environment. You'll be involved in: ·Developing machine learning models to improve forecasting and operational planning ·Applying time-series analysis and feature engineering techniques across large datasets ·Building and improving discrete event simulation models to test different scenarios ·Identifying trends, patterns and potential operational constraints … Python experience beneficial) ·SQL and working with large datasets ·Ability to explain technical findings clearly to different audiences A background within transport, logistics, aviation, engineering, manufacturing, supply chain or similar operational environments would be highly advantageous. This would suit a Data Scientist who enjoys modelling how things work, testing ...

About the role Join us as a Software Engineer in our critical Scam Prevention team. This is a high-impact engineering role with a strong product focus, dedicated to solving complex, real-world adversarial problems to directly protect our global customer base. How we work: The Scam Prevention Team … securing customers' money, accelerating product growth by raising automation levels, and helping us engineer a world‐class money transfer product. What do you need? Engineering Expertise: Professional experience with Java or other JVM‐based languages, including the Spring framework. Experience building and designing scalable web applications that handle large ...

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 ...