7 of 7 Permanent Feature Engineering Jobs in the UK excluding London

development experience in Modelling. Advanced proficiency in Python (including libraries such as NumPy, Pandas, scikit‐learn, PyTorch/TensorFlow). Strong understanding of feature engineering, model selection, evaluation metrics, optimisation techniques and Gen AI. Ability to apply complex statistical methods and design experiments. Familiarity with GCP cloud environment ...

operational use, including synthetic data generation. Build and productionise ML/NLP solutions for legal and business use cases to enable better decision making. Feature Engineering & Integration : Develop new features, integrate data from internal/external sources, and create unified views to enhance performance and outcomes. Pipeline Development ...

cloud and on-prem data platforms , automation, and emerging AI-driven capabilities, and is specifically suited to candidates with strong, hands-on data engineering experience , particularly in building, operating, and optimising complex data pipelines, data models, and integration workflows at scale. Role Overview and Responsibilities As a Data & Automation … delivery processes using technologies such as Azure Data Factory, Databricks, SSIS, and SQL . Reducing manual interventions through automation and standardisation , including data preparation, feature engineering, and training-data pipelines. Preparing data models and datasets (DWH/Lakehouse) to support business intelligence, analytics, and operational reporting. Monitoring ...

proof-of-concept development using Databricks notebooks, analytical outputs and machine learning techniques to validate product concepts Own technical product definition, including data structures, feature sets, scoring methodologies, model architectures and delivery formats in collaboration with the Product Team Define and monitor technical success metrics (data coverage, refresh latency … needs into technical product requirements Support rapid prototyping and proof-of-concept development using Databricks notebooks and analytics outputs Define product-level data structures, feature sets, scoring outputs and delivery formats in collaboration with the Product Team Product Launch & Performance responsibilities: Define technical success metrics (data coverage, refresh latency ...

Forum, Metro Ethernet Forum, IETF, GSMA, Open Broadband etc. Working knowledge of classical AI and machine learning techniques, including supervised and unsupervised learning, feature engineering, model training, evaluation and deployment. Working knowledge of next‐generation AI approaches, including generative AI and agentic systems, such as large language models ...

Forum, Metro Ethernet Forum, IETF, GSMA, Open Broadband etc. Working knowledge of classical AI and machine learning techniques, including supervised and unsupervised learning, feature engineering, model training, evaluation and deployment. Working knowledge of next‐generation AI approaches, including generative AI and agentic systems, such as large language models ...

Forum, Metro Ethernet Forum, IETF, GSMA, Open Broadband etc. Working knowledge of classical AI and machine learning techniques, including supervised and unsupervised learning, feature engineering, model training, evaluation and deployment. Working knowledge of next‐generation AI approaches, including generative AI and agentic systems, such as large language models ...