1 to 25 of 64 Feature Engineering Jobs in London

an innovative team shaping next-generation solutions in the Telecoms sector. Key Responsibilities - Design, build and deploy AI/ML models, including LLMs and generative AI - Perform data exploration, feature engineering, and model optimisation - Apply supervised, unsupervised, and reinforcement learning algorithms - Conduct large-scale data analysis across structured and unstructured sources - Develop statistical and predictive models to drive More ❯

AI. The team includes data strategists, consultants, data scientists and analysts that work alongside DEPT teams around the world across different services - from commerce, to full-funnel media, content engineering to internal operations. You will be solving some of the hardest and most challenging problems facing some of the best loved brands in the world - and doing this alongside … machine learning models for marketing attribution, customer prediction, segmentation, and recommendation systems with deep understanding of customer experience and marketing use-cases Implement end-to-end ML pipelines including feature engineering, model training, deployment, and monitoring Perform advanced statistical analysis including A/B testing, causal inference, and experimental design Deploy production ML systems with automated retraining, drift … on data and ML implementation, serving as a trusted advisor on data-driven decision making WHAT WE ARE LOOKING FOR MSc degree in Data Science, Statistics, Computer Science, Mathematics, Engineering, or a related quantitative field 3+ years of experience in data science and/or ML engineering roles Strong foundation in statistics and mathematics including probability theory, hypothesis More ❯

Build interactive front-ends (Tableau, Streamlit, Plotly Dash) so non-technical users can explore results intuitively Turn insights into actionable recommendations and present findings to clients; Contribute reusable components (feature engineering blocks, forecasting engines, GenAI pipelines) to our internal AI/ML toolkit Support business-development by shaping analytics in proposals and thought-leadership Tech you'll use … to pick up new languages and frameworks quickly. What we're looking for Degree (2:1 or above) or Master's in a quantitative field (Data Science, Computer Science, Engineering, Mathematics, Physics, Economics etc.) 0-2 yrs experience applying Python/SQL & core ML to real-world data sets Confidence explaining technical concepts to senior business audiences Intellectual curiosity More ❯

and technologies that you can then put into practice and become certified on various Cloud (and related) technologies that will help you to develop your own toolkit. Requirements Software Engineering: Proficiency in programming languages used in ML, such as Python/Java. Knowledge of software development best practices and methodologies. Experience with version control systems (e.g., Git). Familiarity … ML): Deep understanding of machine learning principles, algorithms, and techniques. Experience with popular ML frameworks and libraries like TensorFlow, PyTorch, scikit-learn, or Apache Spark. Proficiency in data preprocessing, feature engineering, and model evaluation. Knowledge of ML model deployment and serving strategies, including containerization and microservices. Familiarity with ML lifecycle management, including versioning, tracking, and model monitoring. Ability … as an MLOps Engineer or in a similar role, with an excellent understanding of AI/ML lifecycle management. Strong experience deploying and productionising ML models. Familiarity with data engineering concepts, including data pipelines, ETL processes, and big data technologies. Excellent problem-solving skills and the ability to troubleshoot complex issues in AI/ML systems. Technical Insight Skills More ❯

and technologies that you can then put into practice and become certified on various Cloud (and related) technologies that will help you to develop your own toolkit. Requirements Software Engineering: • Proficiency in programming languages used in ML, such as Python/Java. • Knowledge of software development best practices and methodologies. • Experience with version control systems (e.g., Git). • Familiarity … ML): • Deep understanding of machine learning principles, algorithms, and techniques. • Experience with popular ML frameworks and libraries like TensorFlow, PyTorch, scikit-learn, or Apache Spark. • Proficiency in data preprocessing, feature engineering, and model evaluation. • Knowledge of ML model deployment and serving strategies, including containerization and microservices. • Familiarity with ML lifecycle management, including versioning, tracking, and model monitoring. • Ability … as an MLOps Engineer or in a similar role, with an excellent understanding of AI/ML lifecycle management. • Strong experience deploying and productionizing ML models. • Familiarity with data engineering concepts, including data pipelines, ETL processes, and big data technologies. • Excellent problem-solving skills and the ability to troubleshoot complex issues in AI/ML systems. Technical Insight • Skills More ❯

developing and integrating cutting-edge AI solutions-including LLMs and AI agents -into our products and operations at a leading SaaS company. You'll collaborate closely with product and engineering teams to deliver innovative, high-impact systems that push the boundaries of AI in rebate management. This is a highly collaborative and fast-moving environment where your contributions will … or in collaboration with other specialists. Optimize model pipelines for latency, scalability, and cost-efficiency , and support real-time and batch inference needs. Collaborate with MLOps, DevOps, and data engineering teams to ensure reliable model deployment and system integration. Stay informed on current research and emerging tools in LLMs, generative AI, and autonomous agents , and evaluate their practical applicability. … Participate in roadmap planning, design reviews, and documentation to ensure robust and maintainable systems. Required Qualifications 5+ years of experience in machine learning engineering, applied AI, or related fields. Bachelor's or Master's degree in Computer Science, Machine Learning, Engineering , or a related technical discipline. Strong foundation in machine learning and data science fundamentals -including supervised/ 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 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 ❯

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 ❯

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 ❯

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 ❯

apply machine learning techniques to real-world problems, shipping results fast, all whilst meeting launch deadlines. - Take ownership of end-to-end ML model development-from data preprocessing and feature engineering to training, testing, and deployment. - Collaborate across teams to implement machine learning solutions into production systems, ensuring that models are scalable, reliable, and effective. - Actively contribute to … years of experience in machine learning, with a strong understanding of core ML algorithms and frameworks (e.g., scikit-learn, TensorFlow, PyTorch). - Hands-on experience with data preprocessing, feature engineering, and model training for real-world problems. - Strong Python and Java programming skills and familiarity with NLP algorithms and libraries. - Solid understanding of basic statistics and how to More ❯

apply machine learning techniques to real-world problems, shipping results fast, all whilst meeting launch deadlines. - Take ownership of end-to-end ML model development-from data preprocessing and feature engineering to training, testing, and deployment. - Collaborate across teams to implement machine learning solutions into production systems, ensuring that models are scalable, reliable, and effective. - Actively contribute to … years of experience in machine learning, with a strong understanding of core ML algorithms and frameworks (e.g., scikit-learn, TensorFlow, PyTorch). - Hands-on experience with data preprocessing, feature engineering, and model training for real-world problems. - Strong Python and Java programming skills and familiarity with NLP algorithms and libraries. - Solid understanding of basic statistics and how to More ❯

apply machine learning techniques to real-world problems, shipping results fast, all whilst meeting launch deadlines. - Take ownership of end-to-end ML model development-from data preprocessing and feature engineering to training, testing, and deployment. - Collaborate across teams to implement machine learning solutions into production systems, ensuring that models are scalable, reliable, and effective. - Actively contribute to … years of experience in machine learning, with a strong understanding of core ML algorithms and frameworks (e.g., scikit-learn, TensorFlow, PyTorch). - Hands-on experience with data preprocessing, feature engineering, and model training for real-world problems. - Strong Python and Java programming skills and familiarity with NLP algorithms and libraries. - Solid understanding of basic statistics and how to More ❯

apply machine learning techniques to real-world problems, shipping results fast, all whilst meeting launch deadlines. - Take ownership of end-to-end ML model development-from data preprocessing and feature engineering to training, testing, and deployment. - Collaborate across teams to implement machine learning solutions into production systems, ensuring that models are scalable, reliable, and effective. - Actively contribute to … years of experience in machine learning, with a strong understanding of core ML algorithms and frameworks (e.g., scikit-learn, TensorFlow, PyTorch). - Hands-on experience with data preprocessing, feature engineering, and model training for real-world problems. - Strong Python and Java programming skills and familiarity with NLP algorithms and libraries. - Solid understanding of basic statistics and how to More ❯

apply machine learning techniques to real-world problems, shipping results fast, all whilst meeting launch deadlines. Take ownership of end-to-end ML model development-from data preprocessing and feature engineering to training, testing, and deployment. Collaborate across teams to implement machine learning solutions into production systems, ensuring that models are scalable, reliable, and effective. Actively contribute to … years of experience in machine learning, with a strong understanding of core ML algorithms and frameworks (e.g., scikit-learn, TensorFlow, PyTorch). Hands-on experience with data preprocessing, feature engineering, and model training for real-world problems. Strong Python and Java programming skills and familiarity with NLP algorithms and libraries. Solid understanding of basic statistics and how to More ❯

SageMaker (moving to Azure ML); containerise code and hook into CI/CD. Monitoring & tuning - track drift, response quality and spend; implement automated retraining triggers. Collaboration - work with Data Engineering, Product and Ops teams to translate business constraints into mathematical formulations. Tech stack Python (pandas, NumPy, scikit-learn, PyTorch/TensorFlow) SQL (Redshift, Snowflake or similar) AWS SageMaker → Azure … clean, tested code. Cloud ML: hands-on with AWS SageMaker plus exposure to Azure ML; Docker, Git, CI/CD, Terraform. SQL mastery for heavy-duty data wrangling and feature engineering. Experimentation chops - offline metrics, online A/B test design, uplift analysis. Production mindset: containerise models, deploy via Airflow/ADF, monitor drift, automate retraining. Soft skills: clear More ❯

SageMaker (moving to Azure ML); containerise code and hook into CI/CD. Monitoring & tuning - track drift, response quality and spend; implement automated retraining triggers. Collaboration - work with Data Engineering, Product and Ops teams to translate business constraints into mathematical formulations. A typical day Morning stand-up: align on performance targets and new constraints. Data dive: explore panel behaviour … clean, tested code. Cloud ML: hands-on with AWS SageMaker plus exposure to Azure ML; Docker, Git, CI/CD, Terraform. SQL mastery for heavy-duty data wrangling and feature engineering. Experimentation chops - offline metrics, online A/B test design, uplift analysis. Production mindset: containerise models, deploy via Airflow/ADF, monitor drift, automate retraining. Soft skills: clear More ❯

SageMaker (moving to Azure ML); containerise code and hook into CI/CD. Monitoring & tuning - track drift, response quality and spend; implement automated retraining triggers. Collaboration - work with Data Engineering, Product and Ops teams to translate business constraints into mathematical formulations. A typical day Morning stand-up: align on performance targets and new constraints. Data dive: explore panel behaviour … clean, tested code. Cloud ML: hands-on with AWS SageMaker plus exposure to Azure ML; Docker, Git, CI/CD, Terraform. SQL mastery for heavy-duty data wrangling and feature engineering. Experimentation chops - offline metrics, online A/B test design, uplift analysis. Production mindset: containerise models, deploy via Airflow/ADF, monitor drift, automate retraining. Soft skills: clear More ❯

We work closely with stakeholders across the business to expand the understanding and impact of machine learning and AI throughout Trainline. The Role We are looking for a MLOps Engineering Manager to join our team and help shape the future of train travel. You will be part of a highly innovative AI and ML team working alongside engineers, scientists … the opportunity to work with fellow ML enthusiasts on large-scale production systems, delivering highly impactful products that make a difference to our millions of users. As a MLOps Engineering Manager at Trainline you will Build a new team of MLOps Engineers working alongside ML Engineers, Data Engineers, Software Engineers, Data Scientists and Product Managers Define MLOps processes and … machine learning products Ensure delivery of high-quality, scalable and maintainable machine learning models and AI Systems that drive measurable impact for our business Act as a bridge between engineering and data, ensuring engineering standards are met while understanding the specificities of data, AI and machine learning challenges Take an active part in our AI and ML community More ❯

modelling, and asset behaviour analytics Conduct advanced experimentation and model development, applying deep learning, LLMs, or probabilistic methods as appropriate Build high-performance, production-grade pipelines for data ingestion, feature engineering, model training, and real-time inference Develop and maintain robust APIs and infrastructure for scalable model deployment, monitoring, and versioning Apply rigorous model evaluation, performance tuning, and …/B testing methodologies Continuously monitor model behaviour post-deployment, addressing drift, feedback loops, and retraining strategies Contribute to and influence architectural decisions regarding ML systems and tooling (e.g., feature stores, orchestration frameworks, vector databases) Lead technical discussions, code reviews, and mentoring sessions for junior and mid-level data scientists Lead R&D initiatives by exploring and prototyping novel … machine learning, including supervised/unsupervised learning, NLP, and/or deep learning Experience with large language models (LLMs), transformers, and modern NLP techniques (e.g., fine-tuning, embeddings, prompt engineering) Proven track record in designing, scaling, and maintaining ML systems in production (cloud-native solutions preferred – e.g., Azure, AWS, GCP) Excellent problem-solving skills and a passion for innovation More ❯

modelling, and asset behaviour analytics Conduct advanced experimentation and model development, applying deep learning, LLMs, or probabilistic methods as appropriate Build high-performance, production-grade pipelines for data ingestion, feature engineering, model training, and real-time inference Develop and maintain robust APIs and infrastructure for scalable model deployment, monitoring, and versioning Apply rigorous model evaluation, performance tuning, and …/B testing methodologies Continuously monitor model behaviour post-deployment, addressing drift, feedback loops, and retraining strategies Contribute to and influence architectural decisions regarding ML systems and tooling (e.g., feature stores, orchestration frameworks, vector databases) Lead technical discussions, code reviews, and mentoring sessions for junior and mid-level data scientists Lead R&D initiatives by exploring and prototyping novel … machine learning, including supervised/unsupervised learning, NLP, and/or deep learning Experience with large language models (LLMs), transformers, and modern NLP techniques (e.g., fine-tuning, embeddings, prompt engineering) Proven track record in designing, scaling, and maintaining ML systems in production (cloud-native solutions preferred – e.g., Azure, AWS, GCP) Excellent problem-solving skills and a passion for innovation More ❯

modelling, and asset behaviour analytics Conduct advanced experimentation and model development, applying deep learning, LLMs, or probabilistic methods as appropriate Build high-performance, production-grade pipelines for data ingestion, feature engineering, model training, and real-time inference Develop and maintain robust APIs and infrastructure for scalable model deployment, monitoring, and versioning Apply rigorous model evaluation, performance tuning, and …/B testing methodologies Continuously monitor model behaviour post-deployment, addressing drift, feedback loops, and retraining strategies Contribute to and influence architectural decisions regarding ML systems and tooling (e.g., feature stores, orchestration frameworks, vector databases) Lead technical discussions, code reviews, and mentoring sessions for junior and mid-level data scientists Lead R&D initiatives by exploring and prototyping novel … machine learning, including supervised/unsupervised learning, NLP, and/or deep learning Experience with large language models (LLMs), transformers, and modern NLP techniques (e.g., fine-tuning, embeddings, prompt engineering) Proven track record in designing, scaling, and maintaining ML systems in production (cloud-native solutions preferred – e.g., Azure, AWS, GCP) Excellent problem-solving skills and a passion for innovation More ❯