13 of 13 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 ...

bridge the gap between machine learning, data science, and engineering. What you’ll do Shape the architecture of data products built for: analytics forecasting feature engineering customer behavior insights integration of new data sources Data Transformation & Governance Lead best practices in: data modelling performance optimization governance consistent reliable … Explore open-source tools Engage with the broader tech community Help keep Prima at the cutting edge What we’re looking for Software & Data Engineering Strong expertise in Python Passion for writing: clean reliable production-grade code Distributed Data Processing Experience building and operating: batch processing systems distributed data ...

experimenting with new estimators, models, and features to boost payment performance across our merchants portfolio. You will work closely with Data Scientists, Product, and Engineering to enhance our core offering, protect customer lifetime value through network intelligence, and ensure safe model launches through robust observability. Key Responsibilities Contribute … other Data Scientists and engineers to productionise ML features, models, and evolve our evaluation and monitoring frameworks. Write high‐quality, interpretable Python code for feature engineering and model training, contributing directly to our core products. Communicate hypotheses, evaluation results, and monitoring dashboards clearly to both 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 ...

distributed computing (e.g., Spark, Dask) and ensure robust test coverage for production systems. Mentor junior team members and utilise model explainability methods to drive feature performance improvements. About You 5+ years of experience in designing, building and maintaining machine learning models to solve complex, large-scale business problems. Deep … statistics, plus supervised and unsupervised modelling techniques. Proven experience modelling complex interactions (e.g., cluster or network effects). Expertise in model explainability to tune feature engineering and understand feature interactions. Ability to write high-quality, production-grade Python code and collaborate effectively with non-technical stakeholders. Proficient ...

Monitoring (TM)solution, you will own the tactical execution of features in an enterprise-grade AML/CLMplatform.You’llwork closely with cross-functional teams (engineering,customers, productmanagersandclient solutions) to deliver mission-critical capabilities:detectionengines,AI models,AI agents,tuning & calibration,alertand case managementUX, audit and reporting. You will translate … evolving threatsusing ground-breaking technology. Key Responsibilities Decompose high-level features/requirementsinto user stories, acceptance criteria, and clear backlog prioritization. Collaborate with engineering, data science, andsolutions teamsto deliver high-quality TM features (real-time and batch pipelines,detection analytics,alert scoring,alert/case creation, feedback loops). ...

Build and maintain end-to-end ML pipelines covering data preparation, experimentation, training, evaluation, deployment, monitoring, and optimisation. Develop large-scale data processing and feature engineering solutions using technologies such as PySpark and Databricks. Create multimodal machine learning models that combine image, text, metadata, and behavioural data … productionise innovative machine learning solutions. YOUR KEY SKILLS Significant experience designing, deploying, and supporting machine learning systems in production environments. Strong Python software engineering expertise, including testing, code quality, and maintainable application development. Proven experience working with large-scale data processing technologies such as PySpark and Databricks. Strong background ...

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 ...

extraction/resolution Knowledge Graphs; building and inference, graphRAG Recommendation systems and ranking: personalisation, learning-to-rank, feed ranking, user modelling, etc. Tabular ML: feature engineering, structured data modelling (GBDTs, etc.) Statistical & Predictive Modelling: supervised ML, anomaly detection, temporal modelling, etc. Deep understanding of the GenAI landscape ...

scale retrieval. Mentor, coach, and set technical direction within the Recommendations team, helping others grow and innovate. Collaborate closely with cross-functional partners (product, engineering, data) to define problems, translate them into scalable solutions, and deliver measurable business outcomes. Lead the end-to-end lifecycle of ML projects: from … ideation, data acquisition, feature engineering, training, and evaluation to deployment and ongoing iteration. Drive innovation in recommendation systems by researching and integrating emerging ML techniques, frameworks, and tooling, while contributing technical expertise to long-term product and data strategy. Act as a thought leader in the recommendations space ...

procurement. We are seeking an experienced data scientist to develop forecast models for energy, metals, and other inputs, owning the full modelling lifecycle from feature engineering to deployment. You will integrate diverse data sources and translate probabilistic forecasts into procurement decisions, collaborating closely with our engineering team ...

working closely with Reserving and Claims Analytics teams. Own documentation and governance for model approval ahead of deployment. Test new data sources, factors and feature engineering to continually improve model predictiveness. Present model performance against agreed KPIs to key stakeholders. Mentor junior analysts, embedding best practice across ...

these connect to our users’ financial wellbeing through the product experience Advanced Python and SQL skills, and a strong understanding of dbt and Analytics Engineering for feature engineering. In-depth knowledge of varied statistical and machine learning techniques. You're capable across the full model lifecycle, including ...