26 to 41 of 41 Feature Engineering Jobs in the UK

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 ...

translate insights into business impact. Key Responsibilities Design and implement predictive and prescriptive models for MRO AI Solutions. Perform exploratory data analysis and feature engineering. Collaborate with Data Engineers to ensure data readiness for modeling. Communicate findings and recommendations to business stakeholders. Continuously improve models based on feedback ...

pipelines. • Contribute to proprietary data assistance products designed for analytical workflows. • Work across Python‐based modelling, experimentation, and error analysis. • Collaborate closely with engineering teams while leveraging an existing deployment function. • Support continuous improvement across modelling strategies, feature engineering, and evaluation methods. Your Skills and Experience • Strong ...

long-term operational ownership. As our new Data Scientist (Machine Learning) you will be responsible for: Build predictive, forecasting and anomaly-detection models Perform feature engineering & validation using Python/PySpark Work in Fabric Notebooks, Delta Lakehouse, and AutoML Embed models into production pipelines with our Data Engineering ...

long-term operational ownership. As our new Data Scientist (Machine Learning) you will be responsible for: Build predictive, forecasting and anomaly-detection models Perform feature engineering & validation using Python/PySpark Work in Fabric Notebooks, Delta Lakehouse, and AutoML Embed models into production pipelines with our Data Engineering ...

ownership and production-grade model deployment. The Role As Senior Data Scientist, you will: Own end-to-end ML solutions -from problem framing and feature engineering to deployment, monitoring, and governance Translate business objectives into modelling strategies aligned to risk appetite and operational constraints Build and deploy models … using Python, SQL, and AWS (SageMaker or equivalent) Partner closely with Engineering, Data, and Risk/Financial Crime teams to ensure robust, production-ready solutions Establish monitoring frameworks for performance, drift, and retraining Drive clear documentation, traceability, and governance appropriate for a regulated environment This role requires someone ...

will: Lead and develop a growing Data Science team, setting standards and delivery cadence Own end-to-end ML solutions -from problem framing and feature engineering to deployment, monitoring, and governance Translate business objectives into modelling strategies aligned to risk appetite and operational constraints Build and deploy models … using Python, SQL, and AWS (SageMaker or equivalent) Partner closely with Engineering, Data, and Risk/Financial Crime teams to ensure robust, production-ready solutions Establish monitoring frameworks for performance, drift, and retraining Drive clear documentation, traceability, and governance appropriate for a regulated environment This role requires someone ...

based MMM notebooks to incorporate additional media channels Prepare and validate input data from multiple sources prior to modelling Perform data cleaning, transformation, and feature engineering Run, calibrate, and sense-check MMM model outputs Support model validation and quality checks to ensure outputs are robust and usable Document ...

skills with experience querying large scale relational databases such as Azure SQL or PostgreSQL Experience working with high volume or time based data, including feature engineering Experience with simulation, discrete event simulation, or operations research techniques is highly desirable Ability to communicate complex analysis clearly to non-technical ...

modelling solutions Contribute to model documentation standards and lifecycle governance Own backlog contributions, user stories and acceptance criteria Drive alignment between business stakeholders and engineering team Skills & Competencies Strong knowledge of energy markets, including supply demand fundamentals, storage, flows and pricing Proven background in energy analytics and forecasting, including … scenario modelling Experience with major energy hubs (e.g., TTF, JKM) and cross-commodity linkage Solid understanding of feature engineering, statistical modelling, and ML workflows Strong Python skills (e.g., pandas, NumPy, Jupyter) Strong analytical thinking with the ability to simplify complex concepts If this sounds like a good ...

test hypotheses and inform product decisions. Define success metrics and measure the impact of released product changes. Platform & Data Enablement Work closely with Data Engineering to convert analytical prototypes into production ready pipelines, features and outputs. Ensure product enhancements are designed with data quality, lineage, monitoring and performance … internal and external use. Contribute to improving reuse of shared data assets and reducing duplication across products. Stakeholder Collaboration Work collaboratively with Product, Engineering, Insight and Commercial teams to ensure shared understanding of priorities and constraints. Clearly communicate analytical findings, recommendations and trade-offs to nontechnical stakeholders. Support product ...

audiences Running strategic business reviews alongside the sales team Partnering closely with pre-sales to ensure a smooth customer journey Collaborating with product and engineering teams to share feedback and influence the roadmap Identifying opportunities to grow and expand within existing accounts What We’re Looking For Hands … experience as a Data Scientist, ML Engineer , or working with AI/ML solutions in production Strong understanding of the ML lifecycle (feature engineering, training, deployment, real-time scoring) Familiarity with LLMs/agentic frameworks such as LlamaIndex, LangGraph, or DSPy Experience in a customer-facing role (Solutions ...

Markets domains (counterparties, instruments, legal entities, regulatory taxonomies). Implement graph-RAG patterns for multi-hop reasoning and relationship-aware retrieval. 3. Vector Database Engineering — Architect and optimise the vector storage layer — embedding model selection, chunking strategies (semantic, hierarchical, sliding window), index tuning (HNSW, IVF-PQ), metadata filtering, hybrid … data governance, Delta Lake for reliable storage, Databricks Vector Search for managed embeddings, and Spark/SQL for large-scale data processing and feature engineering. 6. Query Routing & Orchestration — Implement intelligent query routing agents that decompose complex user questions into sub-queries, determine the optimal retrieval source (vector, graph ...

champion their adoption across teams. Responsibilities •Act as a data strategist to identify, integrate, and leverage new data sources through product capabilities. •Collaborate with engineering teams to build, test, and deploy scalable data products and predictive models. •Execute analytical experiments to address challenges across multiple business domains. •Mine … science or a related field. •Proven expertise in data mining, statistical modelling, and machine learning techniques. •Advanced experience in pattern recognition, predictive modelling, and feature engineering. •Proficiency in Python and SQL, and a solid grasp of software engineering best practices. •Strong understanding of data structures, model evaluation ...

PyTorch, TensorFlow, or scikit-learn. LLM Experience: Proven experience working with large language models (e.g. GPT, Claude, Gemini) in production environments, including prompt engineering, evaluation, and safety considerations. Strong Engineering Skills: Proficiency in Python and TypeScript, with experience building APIs, microservices, and cloud-native applications. Modern AI Tooling … including CI/CD, model versioning, observability, and retraining pipelines. Data Fluency: Skilled in working with structured and unstructured data, including preprocessing and feature engineering. User-Centred Mindset: Ability to translate complex AI capabilities into intuitive and seamless product experiences. Collaborative Approach: Thrives in cross-functional, creative teams ...

prices and select customers, beyond traditional pricing approaches. Work with very large, messy datasets across multiple product lines, leading data exploration, cleaning and feature engineering. Deploy models into production using Python, SQL, Azure ML and an internal deployment platform. Collaborate with pricing, risk and product stakeholders to test ideas … CatBoost, and an interest in geospatial or graph based modelling. Confidence working with large scale, messy, multi source datasets, including complex data cleansing and feature engineering. Experience solving pricing, risk or customer prediction problems and understanding how models drive profit, loss and customer outcomes. How To Apply ...