9 of 9 Permanent Feature Engineering Jobs in Central 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 ...

Responsibilitie sPerform exploratory data analysis (EDA) to uncover trends, anomalies, and opportunitie sDevelop and validate machine learning models (regression, classification, clustering, etc. )Conduct feature engineering, hypothesis testing, and model evaluatio nTranslate business requirements into data science use cases and analytical solution sDeliver actionable insights to drive optimisation, efficiency ...

Responsibilitie sPerform exploratory data analysis (EDA) to uncover trends, anomalies, and opportunitie sDevelop and validate machine learning models (regression, classification, clustering, etc. )Conduct feature engineering, hypothesis testing, and model evaluatio nTranslate business requirements into data science use cases and analytical solution sDeliver actionable insights to drive optimisation, efficiency ...

focus on data and digital capability. You'll sit within a dedicated data function, working closely with a Senior Data Scientist and collaborating across engineering, product and business teams to build models that get used, not just presented. Responsibilities as a Data Scientist: Conduct exploratory data analysis to identify … trends, patterns and anomalies across large datasets Build, refine and deploy machine learning models to solve business problems Perform data cleaning, feature engineering and transformation to ensure data quality and model suitability Support integration of models into business processes and operational systems Monitor deployed model performance and recommend ...

focus on data and digital capability. You'll sit within a dedicated data function, working closely with a Senior Data Scientist and collaborating across engineering, product and business teams to build models that get used, not just presented. Responsibilities as a Data Scientist: Conduct exploratory data analysis to identify … trends, patterns and anomalies across large datasets Build, refine and deploy machine learning models to solve business problems Perform data cleaning, feature engineering and transformation to ensure data quality and model suitability Support integration of models into business processes and operational systems Monitor deployed model performance and recommend ...

business and regulatory challenges .Collaborate with data scientists, engineers, and business stakeholders to build and deploy production-grade machine learning models .Perform data analysis, feature engineering, model training, validation, and optimization .Conduct exploratory data analysis (EDA) to identify patterns, anomalies, and emerging risk indicators .Develop and maintain classification … s.Experience wit h time-series analys is to identify trends and assess risks over tim e.Expertise i n exploratory data analysis (ED A) and feature engineerin g.Strong problem-solving skills and the ability to communicate complex analytical findings to technical and non-technical stakeholder s. ...

business and regulatory challenges .Collaborate with data scientists, engineers, and business stakeholders to build and deploy production-grade machine learning models .Perform data analysis, feature engineering, model training, validation, and optimization .Conduct exploratory data analysis (EDA) to identify patterns, anomalies, and emerging risk indicators .Develop and maintain classification … s.Experience wit h time-series analys is to identify trends and assess risks over tim e.Expertise i n exploratory data analysis (ED A) and feature engineerin g.Strong problem-solving skills and the ability to communicate complex analytical findings to technical and non-technical stakeholder s. ...

experimentation strategy for the product area — designing A/B tests and multi-armed bandit experiments that generate genuine learning about AI feature performance, not just validate existing assumptions Drive the feedback loop strategy — defining how member behaviour is captured, modelled, and fed back into AI ranking systems … context, using results to change direction rather than confirm instinct Technically credible AI and ML fluency — able to read a model card, challenge a feature engineering decision, write testable acceptance criteria for ML features, and explain confidence intervals to non-technical stakeholders, without overstepping into engineering decisions ...

experimentation strategy for the product area — designing A/B tests and multi-armed bandit experiments that generate genuine learning about AI feature performance, not just validate existing assumptions Drive the feedback loop strategy — defining how member behaviour is captured, modelled, and fed back into AI ranking systems … context, using results to change direction rather than confirm instinct Technically credible AI and ML fluency — able to read a model card, challenge a feature engineering decision, write testable acceptance criteria for ML features, and explain confidence intervals to non-technical stakeholders, without overstepping into engineering decisions ...