26 to 50 of 253 Feature Engineering Jobs in the UK

and simultaneously, maintain production models ensuring operational excellence as well as long term strategy. The ideal candidate will have a strong background in machine learning, data science, and software engineering, with the ability to design, develop, and implement robust machine learning models and systems in production. Key Responsibilities Lead the development of machine learning algorithms and models for behavioural … modeling and cybersecurity attack detection. Collaborate with cross-functional teams to understand requirements and translate them into effective machine learning solutions. Conduct exploratory data analysis, feature engineering, model development and evaluation. Work with infrastructure & product engineers to productionize models and new ML based features. Actively monitor and improve production models through feature engineering, rules and ML More ❯

customer problems while maintaining production models to ensure operational excellence and long-term strategy. The ideal candidate will have a strong background in machine learning, data science, and software engineering, with the ability to design, develop, and implement robust machine learning models and systems in production. Key Responsibilities Lead the development of machine learning algorithms and models for behavioral … modeling and cybersecurity attack detection. Collaborate with cross-functional teams to understand requirements and translate them into effective machine learning solutions. Conduct exploratory data analysis, feature engineering, model development, and evaluation. Work with infrastructure and product engineers to productionize models and new ML-based features. Actively monitor and improve production models through feature engineering, rules, and More ❯

sector, specifically in energy systems and grid automation, or related domains such as smart infrastructure or industrial automation. They should have a strong understanding of applying data science and engineering techniques to develop, validate, and improve AI/ML models in complex, data-rich environments. Essential Responsibilities: Design and conduct experiments to test and validate AI/ML models … deployment. Ensure validation processes adhere to data governance policies and standards. Communicate validation results and insights clearly to stakeholders. Must-Have Requirements: Degree in Data Science, Computer Science, Electrical Engineering, or related field with experience in model validation. Experience in the energy sector, especially in energy systems or grid automation. Proven experience validating AI/ML models to meet … requirements. Strong knowledge of statistical techniques and validation methodologies. Proficiency in Python, R, or MATLAB. Experience with data wrangling, feature engineering, and dataset preparation. Knowledge of machine learning frameworks like TensorFlow, PyTorch, Scikit-learn. Experience with cloud platforms (AWS, Azure, GCP) and deploying models. Ability to use data visualization tools like Tableau or Power BI. Nice-to-Have More ❯

these models and LLMs for NLP tasks. Relationship Extraction: Evaluating different models for use-case specific RE, such as ATG. Document and text Classification Data Science: Data clustering algorithms, feature engineering Evaluate and integrate new technologies and models. Cross-team collaboration, identifying innovations and architecting solutions. Provide leadership and technical direction to various business units and partners Why More ❯

and data engineers by ensuring production ready data, model pipelines are deployed to production. Manage the entire model lifecycle using the code frameworks developed by AI Platform team for feature engineering, model training/evaluation, versioning, deployment/online serving, and monitoring prediction quality. Establish and promote best practices in MLOps to streamline model deployment and monitoring across … various environments. Provide authoritative guidance on state-of-the-art algorithms, repositories, and GenAI techniques like prompt engineering, RAG, AI agents in Generative AI space (LLMs) & share your experience with optimisation techniques such as quantization and pruning to minimise training & inference requirements for models. Design and implement effective LLM-tooling, finetuning & response evaluation strategies. Guide teams on the latest … our model development lifecycle and disseminate best practice learnings to colleagues. Help refine and extend our standards and best practices around Machine Learning – training and testing framework, coding standards, engineering practices. Qualifications & Skills: Demonstrate a strong understanding of natural language processing methods, including deep learning techniques, knowledge distillation, prompt engineering, and fine-tuning. 5-8 years of industry More ❯

managers Design and deliver machine learning systems at scale that drive measurable impact for our business Own the full end to end machine learning delivery lifecycle including data exploration, feature engineering, model selection and tuning, offline and online evaluation, deployments and maintenance Partner with stakeholders to propose innovative data products that leverage Trainline’s extensive datasets and state … and foster a culture of rigorous learning and experimentation We'd love to hear from you if you... Have a broad of understanding of data extraction, data manipulation and feature engineering techniques Are familiar with statistical methodologies Are skilled in one of reinforcement learning, predictive modelling (classification and regression) and have a solid understanding of recommendation systems Are … work at Trainline? Why not check us out on LinkedIn, Instagram and Glassdoor! Seniority level Seniority level Entry level Employment type Employment type Full-time Job function Job function Engineering and Information Technology Industries Technology, Information and Internet Referrals increase your chances of interviewing at Trainline by 2x Get notified about new Machine Learning Engineer jobs in London, England More ❯

Machine Learning R&D Engineer (KTP Associate). As a Machine Learning R&D Engineer Associate, you will develop Machine Learning (ML) solutions that will innovate and transform key engineering activities in Buro Happold. You will work closely with Buro Happold’s computational team to build reusable AI/ML datasets, develop and optimise ML models, facilitate their deployment … within the company, and co-define data-driven solutions that have the potential to revolutionise the Architecture, Engineering and Construction (AEC) industry. This role presents an exciting opportunity to work in collaboration with leading academics at Birmingham City University, to apply knowledge and technical innovation, delivered on site at the company. The Machine Learning R&D Engineer (KTP Associate … construction projects (recent award-winning projects include Battersea Power Station in London and the K64 Keflavik Airport Area Masterplan). Buro Happold covers several domains of expertise, including structural engineering, façade design, MEP engineering, and many others. Buro Happold has a long history of developing and applying innovative computational solutions, like the open-source framework Buildings and Habitats More ❯

to develop advanced time-series models for demand forecasting. Collaborate with cross-functional teams to identify and define business problems related to demand forecasting. Conduct exploratory data analysis and feature engineering to extract valuable insights from complex datasets. Develop and implement machine learning algorithms to optimize demand forecasting accuracy and efficiency. Evaluate and fine-tune models by applying More ❯

foundation in machine learning algorithms, statistics, and data structures within relevant technical projects. Proficiency in programming languages, frameworks, and tools, such as Python, TensorFlow, PyTorch. Experience with data preprocessing, feature engineering, and model evaluation techniques. Highly Desirable: Experience working on aerospace-related projects Experience deploying MLOps solutions and working within CI/CD frameworks Experience with Linux systems More ❯

modeling, machine learning, and data mining techniques, with a focus on applications in text analysis or scientific data, including knowledge of forecasting, A/B testing, entity extraction, and feature engineering. Proficiency in programming languages such as Python, R, and SQL, and data analysis libraries (e.g., Pandas, NumPy, SciPy, Tidyverse). Strong knowledge of machine learning frameworks and libraries More ❯

modeling, machine learning, and data mining techniques, with a focus on applications in text analysis or scientific data, including knowledge of forecasting, A/B testing, entity extraction, and feature engineering. Proficiency in programming languages such as Python, R, and SQL, and data analysis libraries (e.g., Pandas, NumPy, SciPy, Tidyverse). Strong knowledge of machine learning frameworks and libraries More ❯

you share our passion for ideas and commitment to excellence, we want you to join us. To learn more visit alti-global.com. Job Description & Overview The Head of Data Engineering & Analytics will lead the development and execution of AlTi’s enterprise data engineering strategy, enabling the capture, transformation, storage and delivery of high-quality data across the firm … s global wealth, investment, corporate and asset management functions. This leader will architect and scale data engineering capabilities to support real-time and batch integration, reporting, and advanced analytics. This role reports to the CTO and will be a key member of the Global Technology Solutions leadership team. In this hands-on leadership role, you will work at the … intersection of data engineering, business intelligence, data science, strategy and governance. The ideal candidate will combine deep technical expertise in cloud data platforms and integration tools with strong experience implementing scalable data pipelines, robust data models, data visualization platforms and governance frameworks. This is a pivotal role in AlTi’s shift toward becoming a data-driven organization, with significant More ❯

Social network you want to login/join with: Head of Data Engineering & Analytics, slough col-narrow-left Client: Location: slough, United Kingdom Job Category: Other - EU work permit required: Yes col-narrow-right Job Views: 4 Posted: 31.05.2025 Expiry Date: 15.07.2025 col-wide Job Description: Company Description AlTi Tiedemann Global (“AlTi”) is a NASDAQ listed global wealth manager … you share our passion for ideas and commitment to excellence, we want you to join us. To learn more visit alti-global.com. Job Description & Overview The Head of Data Engineering & Analytics will lead the development and execution of AlTi’s enterprise data engineering strategy, enabling the capture, transformation, storage and delivery of high-quality data across the firm … s global wealth, investment, corporate and asset management functions. This leader will architect and scale data engineering capabilities to support real-time and batch integration, reporting, and advanced analytics. This role reports to the CTO and will be a key member of the Global Technology Solutions leadership team. In this hands-on leadership role, you will work at the More ❯

and requirements into tasks that can be solved with generative AI, machine learning, or other algorithmic solutions Process structured and unstructured data for generative AI use cases Collaborate with engineering and product development teams Following experimentation and model selection and validation, implement the solution using production-ready Python code Contribute to the full software development lifecycle, including requirements gathering … colleagues and stakeholders Present information using data visualization techniques Qualifications/Requirements: A master’s degree (or equivalent) in a numerate discipline such as Statistics, Machine Learning, Computer Science, Engineering, or Physics. A doctorate is a plus but not required Mid-level professional with 2-4 years of ML/AI experience, typically at an Analyst level role or … external equivalent A scientific approach to solving problems, an analytical mind, and experience in applied machine learning and generative AI Experience in applying LLMs to analyze text documents, prompt engineering, and manual and automatic evaluation of LLM outputs Good foundational knowledge of standard machine learning algorithms and statistical methods Proficient in python and its standard data science and ML More ❯

latency, and compute constraints. Apply and innovate on methods like Bayesian neural networks , variational autoencoders , diffusion models , and Gaussian processes for modern AI use cases. Collaborate closely with product, engineering, and business teams to build end-to-end modeling solutions. Conduct deep-dive statistical and machine learning analyses, simulations, and experimental design. Stay current with emerging trends in generative … TensorFlow Probability , or similar probabilistic programming tools. Hands-on experience with classical ML and modern techniques, including deep learning , transformers , diffusion models , and ensemble methods . Solid understanding of feature engineering, dimensionality reduction, model construction, validation, and calibration. Experience with uncertainty quantification and performance estimation (e.g., cross-validation, bootstrapping, Bayesian credible intervals). Familiarity with database and data … processing tools (e.g., SQL, MongoDB, Spark, Pandas). Ability to translate ambiguous business problems into structured, measurable, and data-driven approaches. Preferred Qualifications M.Sc or PhD in Statistics, Electrical Engineering, Computer Science, Physics, or a related field. Background in generative modeling , Bayesian deep learning , signal/image processing , or graph models . Experience applying probabilistic models in real-world More ❯

and requirements into tasks that can be solved with generative AI, machine learning, or other algorithmic solutions Process structured and unstructured data for generative AI use cases Collaborate with engineering and product development teams Following experimentation and model selection and validation, implement the solution using production-ready Python code Contribute to the full software development lifecycle, including requirements gathering … colleagues and stakeholders Present information using data visualization techniques Qualifications/Requirements A master’s degree (or equivalent) in a numerate discipline such as Statistics, Machine Learning, Computer Science, Engineering, or Physics. A doctorate is a plus but not required Mid-level professional with 2-4 years of ML/AI experience, typically at an Analyst level role or … external equivalent A scientific approach to solving problems, an analytical mind, and experience in applied machine learning and generative AI Experience in applying LLMs to analyze text documents, prompt engineering, and manual and automatic evaluation of LLM outputs Good foundational knowledge of standard machine learning algorithms and statistical methods Proficient in python and its standard data science and ML More ❯

and deliver NLP based machine learning systems at scale that drive measurable impact for our business Own the full end to end machine learning delivery lifecycle including data exploration, feature engineering, model selection and tuning, offline and online evaluation, deployments and maintenance Partner with stakeholders to propose innovative data products that leverage Trainline's extensive datasets and state … practices and platforms like ML Flow Have experience with agile delivery methodologies and CI/CD processes and tools Have a broad understanding of data extraction, data manipulation and feature engineering techniques Are familiar with statistical methodologies. Have good communication skills Nice to have Experience with LangGraph or LangChain Experience with transport industry and/or geographical information More ❯

latency, and compute constraints. Apply and innovate on methods like Bayesian neural networks , variational autoencoders , diffusion models , and Gaussian processes for modern AI use cases. Collaborate closely with product, engineering, and business teams to build end-to-end modeling solutions. Conduct deep-dive statistical and machine learning analyses, simulations, and experimental design. Stay current with emerging trends in generative … TensorFlow Probability , or similar probabilistic programming tools. Hands-on experience with classical ML and modern techniques, including deep learning , transformers , diffusion models , and ensemble methods . Solid understanding of feature engineering, dimensionality reduction, model construction, validation, and calibration. Experience with uncertainty quantification and performance estimation (e.g., cross-validation, bootstrapping, Bayesian credible intervals). Familiarity with database and data … processing tools (e.g., SQL, MongoDB, Spark, Pandas). Ability to translate ambiguous business problems into structured, measurable, and data-driven approaches. Preferred Qualifications: M.Sc or PhD in Statistics, Electrical Engineering, Computer Science, Physics, or a related field. Background in generative modeling , Bayesian deep learning , signal/image processing , or graph models . Experience applying probabilistic models in real-world More ❯

with advancements in machine learning, AI, and data science methodologies to continuously improve processes and solutions Requirements for the role Master’s or Ph.D. in Computer Science, Statistics, Mathematics, Engineering, or a related field (Bachelor’s with significant experience also considered) 3+ years of experience in data science, machine learning, or a related role Proficiency in programming languages such … R, and libraries like TensorFlow, PyTorch, Scikit-learn, or Pandas Strong understanding of statistical modeling, probability, and optimization techniques Ability to work with messy, real-world datasets and perform feature engineering Excellent problem-solving skills and attention to detail Strong communication skills to convey complex concepts to diverse audiences Bonus points You’ve worked in an early-stage More ❯

strong emphasis on mutual assistance. Each team member is approachable and committed to lending a hand, creating an environment where everyone feels supported and valued." - Sreekant, VP of API Engineering The team you'll work with: Reporting to the Director of AI, this is a high-impact role where your expertise will directly shape the future of our ML … value by: Model Development & Deployment: Develop, test, deploy, and maintain machine learning models and algorithms, ensuring their scalability, robustness, and performance in production. Data Analysis & Optimization: Conduct data preprocessing, feature engineering, and exploratory analysis to optimize AI/ML models. Pipeline Development & Enhancement: Design, build, and enhance efficient machine learning pipelines, ensuring their scalability and performance. Collaboration & Cross … technologies in data science and machine learning to identify new opportunities and techniques. To be a successful match you must have: 1+ years in a Machine Learning or ML Engineering role, with hands-on experience in deep learning frameworks (e.g., TensorFlow, PyTorch). Motivated recent graduates are encouraged to apply! A degree in Mathematics, Engineering, Statistics, Computer Science More ❯

data labeling and tagging. Analyze large amounts of information to discover trends and patterns. Present information using data visualization techniques. Propose solutions and strategies to business challenges. Collaborate with engineering and product development teams. Perform feature engineering for AI applications. What Skills Are Required: BS/MS/PhD in Computer Science, Engineering, or relevant field More ❯

with different machine learning techniques and algorithms, including supervised, unsupervised, semi-supervised, reinforcement, and deep learning; Design and optimize machine learning pipelines and workflows, incorporating techniques for data cleaning, feature engineering, model selection, and hyperparameter tuning; and Develop scalable and efficient machine learning infrastructure and systems for training, testing, and deploying models in production environments. Qualifications Bachelor's More ❯

experience in AI, machine learning, or data science. Proficiency in Python and experience with machine learning frameworks like TensorFlow, PyTorch, or Scikit-learn . Strong understanding of data analysis , feature engineering , and model evaluation techniques, particularly with retail and consumer-facing data. Passion for AI and the retail industry, with a desire to help businesses improve operational efficiencies More ❯

Design, build, and deploy ML models for real-time credit risk scoring, fraud detection, and dynamic pricing Architect and implement end-to-end ML pipelines (from data ingestion and feature engineering to monitoring and retraining) Collaborate with product, engineering, and data teams to identify use cases, develop models, and integrate into our core platform Experiment with and … deploying ML models into production environments Familiarity with ML Ops workflows (e.g., MLflow, Airflow, Weights & Biases, or Kubeflow) Experience working with structured data (credit, payments, customer behaviour) and applying feature engineering at scale Understanding of model performance metrics, calibration, A/B testing, and monitoring in production systems Experience with cloud platforms (GCP, AWS or Azure), especially managed … e.g., using LLMs, transformers, text classification Familiarity with Graph ML (e.g., for customer network analysis or fraud detection) Exposure to finance, credit risk modelling , or regulated environments Strong software engineering fundamentals, version control, CI/CD, testing Previous startup experience or entrepreneurial mindset What We Offer: A chance to work on real-world ML problems that power decisions across More ❯

Design, build, and deploy ML models for real-time credit risk scoring, fraud detection, and dynamic pricing Architect and implement end-to-end ML pipelines (from data ingestion and feature engineering to monitoring and retraining) Collaborate with product, engineering, and data teams to identify use cases, develop models, and integrate into our core platform Experiment with and … deploying ML models into production environments Familiarity with ML Ops workflows (e.g., MLflow, Airflow, Weights & Biases, or Kubeflow) Experience working with structured data (credit, payments, customer behaviour) and applying feature engineering at scale Understanding of model performance metrics, calibration, A/B testing, and monitoring in production systems Experience with cloud platforms (GCP, AWS or Azure), especially managed … e.g., using LLMs, transformers, text classification Familiarity with Graph ML (e.g., for customer network analysis or fraud detection) Exposure to finance, credit risk modelling , or regulated environments Strong software engineering fundamentals, version control, CI/CD, testing Previous startup experience or entrepreneurial mindset What We Offer: A chance to work on real-world ML problems that power decisions across More ❯