6 of 6 Remote/Hybrid Feature Engineering Jobs in London

community. Please do not contact the recruiter directly. Your Role This role focuses on applying data science, statistics, machine learning, graph analytics, and KPI engineering to enable autonomous network intelligence. The AI Data Scientist will work with network telemetry, alarms, performance counters, inventory data, topology data, trouble tickets, service … network operations. Key Responsibilities Analyse large-scale telecom network datasets across RAN, Core, IP, Transport, SD-WAN, Cloud, OSS, and service domains. Develop KPI engineering models for network performance, service quality, fault behaviour, customer impact, capacity, and resilience. Build statistical and machine learning models for anomaly detection, fault prediction ...

taxonomies, citations, metadata, and content from every scientific discipline. Apply the right technique to each problem, using approaches such as classification, regression, clustering, ranking, feature engineering, deep learning, embeddings, LLMs, retrieval, and generative AI. Develop capabilities for semantic search, information retrieval, entity extraction, content classification, recommendation, ranking, summarization … inference, experimentation, monitoring, and continuous improvement. Support deployment, monitoring, model maintenance, drift detection, automated retraining, and ongoing optimization of data science systems. Collaborate with engineering, product, UX, analytics, research, and domain experts, and communicate technical concepts, model behavior, insights, trade-offs, and recommendations clearly to technical and non-technical ...

bring them to life, owning the journey from concept to deployment and beyond.Own end-to-end delivery of data science solutions—from problem framing, feature engineering, monitoring, evaluation and deploymentDesign and execute hypothesis-driven analysis and statistical testing (e.g., experiment design where applicable, assumption checking, uncertainty quantification … bring them to life, owning the journey from concept to deployment and beyond.Own end-to-end delivery of data science solutions—from problem framing, feature engineering, monitoring, evaluation and deploymentDesign and execute hypothesis-driven analysis and statistical testing (e.g., experiment design where applicable, assumption checking, uncertainty quantification ...

before production deployment. Develop robust ML pipelines using AWS services and MLOps tooling. Implement experiment tracking, model versioning, monitoring, and reproducibility best practices. Build feature engineering pipelines and contribute to feature store development. Monitor production models, optimise performance, and drive continuous improvements. Apply Responsible AI principles, including … technical concepts to both technical and non-technical stakeholders. Desirable Experience AI agents and agentic workflows. Vector databases such as Pinecone, Weaviate, or pgvector. Feature stores including Feast or AWS Feature Store. Docker, Kubernetes, or Amazon ECS. Infrastructure as Code using Terraform or CloudFormation. Big data processing technologies ...

testing, quasi-experimental methods, and stakeholder-facing insight delivery. Key Responsibilities Analytical discovery and opportunity identification. Value case assessment. Data preparation and ETL support. Feature engineering collaboration with Data Scientists. Testing and evaluation of interventions. Experiment execution and results analysis. Presenting outcomes to senior stakeholders. Influencing business decisions ...

Strong Python and Pandas. Advanced SQL. Experience working with Azure Data Lake or similar cloud data platforms. Experience working with very large datasets. Strong feature engineering experience. Experience improving production classification models. Excellent understanding of F1 Score, Precision, Recall and model evaluation. Experience working with highly imbalanced datasets. ...