4 of 4 Remote/Hybrid Feature Engineering Jobs in London

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 ...

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. ...