76 to 100 of 148 Feature Engineering Jobs in London

customers in more than 120 markets. ## **Role overview:** This is along-term **fixed-term contract** designed to support Planet’s AI-first engineering scale-up project. We're hiring a Data Scientist to join Planet's AI team and lead our commercial data science work. You'll partner … tests and quasi-experiments for commercial initiatives. Bring statistical rigour to business decisions.* **Productionisation** — Take models from notebook to production. Own the full lifecycle: feature engineering, training, deployment, monitoring, retraining.* **Stakeholder partnership** — Translate ambiguous commercial questions into well-scoped analytical problems. Communicate findings clearly to non-technical audiences. ...

Design and develop predictive, machine learning, and Generative AI solutions. Build prototypes, proof-of-concepts, and production-ready analytical models. Perform data exploration, feature engineering, model development, and validation. Collaborate with data engineers and architects to operationalise solutions. Ensure solutions follow Responsible AI, governance, and security best practices. ...

Design and develop predictive, machine learning, and Generative AI solutions. Build prototypes, proof-of-concepts, and production-ready analytical models. Perform data exploration, feature engineering, model development, and validation. Collaborate with data engineers and architects to operationalise solutions. Ensure solutions follow Responsible AI, governance, and security best practices. ...

security, identity and compliance (Entra ID, RBAC, network isolation, data protection). Strong experience designing LLM‐based solutions, including Retrieval‐Augmented Generation (RAG), prompt engineering strategies and agent‐based architectures, leveraging frameworks such as Semantic Kernel, LangChain, and Microsoft Agent Framework. Deep practical experience with classical Machine Learning, including … supervised and unsupervised learning, feature engineering, model selection, training pipelines, evaluation frameworks and production lifecycle management, equivalent in depth to GenAI expertise. Proven ability to assess client use cases and make well‐reasoned architectural decisions between classical ML and Generative AI approaches; and articulate clearly why. Strong experience ...

with minimal supervision, and implement rigorous validation automation (such as backtesting and slice-based evaluation) to prevent data leakage, over-fitting, and production regressions. Engineering Partnership & Handovers: Collaborate closely with Engineering Leads to ensure compute/GPU infrastructure supports model requirements. Clearly define model failure modes, edge cases … build robust fallback systems. Applied Debugging & Guardrails: Diagnose complex algorithmic bugs and implement automated checks for "Silent Failures" (e.g., concept drift or production feature distribution shifts). Lead team-level post-mortems and resolve blocking corrective actions. Career Multiplier: Formally mentor mid-level and associate ML scientists, reviewing their ...

Barcelona, Milan, Edinburgh and Madrid. Now is a brilliant time to join us and help shape the future of travel. Introducing the Embedded Data Engineering in ML Team At the heart of our Data and ML teams, embedded Data Engineers create the pipelines and tables that power business critical … Machine Learning Engineers and Data Scientists to build reliable datasets for ML use cases. You'll also have access to Trainline's wider Data Engineering, Data Platform, and analytics community, working alongside other embedded Data Engineers in ML, including senior and principal engineers. In this role as the Embedded ...

have missed. Develop and evaluate statistical and machine learning models. Build segmentation, propensity, forecasting and personalisation solutions that reach real customers. Apply rigour to feature engineering, validation and model performance. Design and evaluate experiments to understand genuine impact. Work with Data Engineering to create reliable model inputs ...

Data Science team is building a layered reliability platform that shifts CoreWeave from reactive troubleshooting to proactive reliability engineering. The platform spans telemetry ingestion, feature engineering, anomaly detection, failure prediction, distributed straggler detection, and agentic root cause analysis. As a forward deployed function, we partner closely with Fleet … intersection of data science and production systems, deploying advanced statistical models and machine learning methodologies directly within operational environments. You will collaborate closely with engineering and infrastructure teams to optimize GPU utilization, workload scheduling, and system efficiency in real time. You will design experiments, analyze large-scale system telemetry ...

machine learning platforms. Data governance and metadata tooling to improve transparency, trust, and reuse across the organisation. Your Experience 5+ years experience in data engineering, building and maintaining scalable data platforms. Strong SQL and data modelling skills, with experience designing analytical datasets. Experience with modern data stack tools (e.g. … e.g. Cube.js) or metrics‐layer design. Exposure to data governance and metadata tools (e.g. OpenMetadata, Hive Metastore). Experience supporting machine learning workflows and feature engineering pipelines. Why This Role Matters This platform is central to how we enable trusted, scalable, and reusable data across sportsbook. It provides ...

approaches (e.g. sequence prediction, intent modelling, imitation learning) into robust, production‐ready systems. Build and maintain end‐to‐end AI pipelines, including data ingestion, feature engineering, model training, inference, and monitoring. Apply causal inference techniques to evaluate the real‐world impact of AI‐driven decisions and support data … informed product changes. Integrate AI services into existing platforms via APIs, microservices, and event‐driven architectures in close collaboration with engineering teams. Partner with product and platform teams to ensure AI outputs are actionable, explainable, and aligned with business workflows. Support Digital Twin and agentic systems by implementing behavioural ...

production-grade ML solutions, working closely with Data Scientists to take models from development through to reliable production environments. This is a hands-on engineering role focused on ML pipelines, productionisation, deployment and ongoing model lifecycle management within a modern Databricks environment. Key Responsibilities of the ML Engineer: Build … automate end-to-end ML pipelines covering feature engineering, model training, scoring and deployment Productionise models developed by Data Scientists, transforming notebooks and prototypes into modular, tested and production-ready code Develop scalable ML solutions using Python, PySpark, Databricks and MLflow Deploy machine learning models into batch ...

Responsibilities: Develop and implement machine learning and AI models Develop test and evaluation protocols on ML/AI models Assist in data pre-processing, feature engineering, and exploratory data analysis Contribute to building scalable and maintainable AI software systems Debug, test, and improve existing models and codebases Document … soon as practical. About Us The National Physical Laboratory (NPL) is a world-leading centre of excellence that provides cutting-edge measurement science, engineering and technology to underpin prosperity and quality of life in the UK. NPL and DSIT have strong commitments to diversity and equality of opportunity ...

deliver modern Machine Learning solutions that improve recommendations, matching and personalisation across Bumble. Own defined ML problems end-to-end, from data exploration and feature engineering through model training, evaluation, production deployment and iteration. Use modern ML frameworks such as PyTorch or TensorFlow to design, train and optimise … production models, diagnosing issues and improving reliability and performance at scale. Write high-quality, maintainable code and contribute to code reviews, technical discussions and engineering best practices. Take ownership of delivering high-quality solutions from insight through to measurable impact, balancing speed, quality and technical rigour. Apply responsible ...

investigate problems, test hypotheses and develop analytical solutions. Monitor production models, investigate changes in performance and identify opportunities for improvement. Undertake exploratory analysis, feature engineering, model development, testing and validation. Evaluate new modelling techniques, AI applications and external data sources through practical experimentation. Support the deployment and ongoing ...

Bring Hands‐on experience in data science or applied machine learning. Strong understanding of Generative AI, LLMs, and RAG principles. Experience with data mapping, feature engineering, and end‐to‐end ML lifecycle management. Proficiency across Microsoft's Azure AI and data ecosystem, with an eagerness to adopt Fabric ...

financial services (ideally Anti-Financial Crime/Screening) Experience building Natural Language Processing (NLP)/Machine Learning (ML)/Artificial Intelligence (AI) pipelines, including feature engineering, model training, evaluation and monitoring Experience with hyperparameter tuning at scale and experimentation frameworks Strong technical experience with SQL and Python; significant ...

with common ML/data science frameworks. Experience developing models for threat detection and anomaly detection . Experience with security risk analytics . Strong feature engineering experience. Experience working with cyber security data, logs or telemetry . Experience working with threat intelligence data . Experience with graph analytics … cloud AI certification. CISSP, Security+ or equivalent security certification. Graph technology certification. Data platform certification. Experience working directly with security analysts and cyber security engineering teams. Experience supporting responsible AI adoption within security operations. ...

cycles to ensure all models maintain the high level of accuracy and reliability required for professional-grade execution. Cross Functional Integration: Work closely with Engineering and Product teams to ensure seamless delivery of these complex feeds into client workflows, while maintaining the integrity of our internal IP. Cricket & Betting … machine learning and statistics. Practical Experience: Proven track record (2+ years) of applying data science methods as part of end-to-end pipelines (feature engineering, model training, deployment, validation) to solve complex real-world problems in a professional environment. Programming Fluency: Strong programming skills in Python ...

Join a pioneering biotechnology company where your data engineering expertise will help drive innovation in gene therapy and improve the lives of patients with rare diseases. R&D Partners is seeking a Scientific Data Engineer to join an ambitious biotechnology company specializing in gene therapy and rare diseases. This … solutions to improve access to operational and scientific data across the R&D organization. Support data analysis activities by performing data mining, data exploration, feature engineering, and contributing to AI/ML, statistical, and modeling projects alongside data scientists and laboratory scientists. Ensure data quality and integrity throughout ...

forecasting, behavioural signals, and prediction, ensuring they are accurate, explainable, resilient, and regulator-ready. Own the full model lifecycle from problem framing, data exploration, feature engineering, back-testing, validation, deployment, monitoring, tuning, and incident response when models degrade or fail in production. Partner closely with engineering, data … Strong hands‐on technical skills, with the ability to work directly with data, code, ML frameworks, and complex datasets, while partnering effectively with software engineering and platform teams. Experience building scalable, reliable, and explainable AI/ML solutions that meet operational, governance, and regulatory requirements and deliver measurable business ...

forecasting, behavioural signals, and prediction, ensuring they are accurate, explainable, resilient, and regulator-ready. Own the full model lifecycle from problem framing, data exploration, feature engineering, back-testing, validation, deployment, monitoring, tuning, and incident response when models degrade or fail in production. Partner closely with engineering, data … Strong hands-on technical skills, with the ability to work directly with data, code, ML frameworks, and complex datasets, while partnering effectively with software engineering and platform teams. Experience building scalable, reliable, and explainable AI/ML solutions that meet operational, governance, and regulatory requirements and deliver measurable business ...

experimental output computationally actionable. Relocation? Yes Compensation £50k-£70k, depending on experience, plus equity & benefits Location London Reports To Saif Ur-Rehman (Director, Data Engineering) Responsibilities Multi-omic data analysis and interpretation. You own the computational analysis of datasets generated by our wet lab (metagenomics, metabolomics, 16S), from … feature extraction through statistical analysis and biological interpretation. Computational studies. You run your own proof-of-concept investigations: microbiome-compound interactions, community dynamics, predictive biomarkers. You're a scientist with hypotheses, not a technician with a task list. Perturbation data quality. You own the validation layer between the bench ...

production-grade ML solutions, working closely with Data Scientists to take models from development through to reliable production environments. This is a hands-on engineering role focused on ML pipelines, productionisation, deployment and ongoing model lifecycle management within a modern Databricks environment. Key Responsibilities of the ML Engineer: Build … automate end-to-end ML pipelines covering feature engineering, model training, scoring and deploymentProductionise models developed by Data Scientists, transforming notebooks and prototypes into modular, tested and production-ready codeDevelop scalable ML solutions using Python, PySpark, Databricks and MLflowDeploy machine learning models into batch and real-time environments ...

Experiment with supervised, unsupervised, semi-supervised, reinforcement, and deep learning techniques and algorithms Design and optimize machine learning pipelines and workflows, including data cleaning, feature engineering, model selection, and hyperparameter tuning Develop scalable and efficient machine learning infrastructure and systems for training, testing, and deploying models in production ...

expand their machine learning skills. Responsibilities Create statistical summaries to support hypothesis testing and data-driven decisions during EDA Implement data preparation and feature engineering pipelines for the models Plan and implement algorithms for predictive modeling. Provide continuous improvement of models in production Lead the ML-driven python ...