17 of 17 Remote/Hybrid Feature Engineering Jobs in London

Experience designing and analysing A/B tests, defining success metrics, understanding selection bias, and evaluating ranking systems using both online and offline methodologies. Feature Engineering & Applied Machine Learning : Strong background in feature engineering, tabular machine learning, calibrated scoring systems, and production use of models such … Entity‐Aware Ranking : Ability to incorporate relationships between companies, sponsors, lenders, law firms, instruments, documents, and events into ranking and recommendation decisions. Production ML Engineering : Experience building feature pipelines, online and batch inference systems, low‐latency ranking services, experimentation platforms, and monitoring for production machine learning systems. Strong ...

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 ...

executing actions across enterprise systems. Integrate AI solutions with upstream and downstream applications through APIs, event-driven architectures, and workflow orchestration platforms. Model Capability Engineering Select, configure, fine-tune, and evaluate AI/ML models (e.g., LLMs, embeddings, and classifiers) aligned with business requirements. Design and manage Retrieval-Augmented … Generation (RAG) pipelines, prompt engineering strategies, and context management frameworks. Optimize model performance, cost efficiency, latency, scalability, and reliability for production environments. Product ionization & MLOps Operationalize AI solutions through robust monitoring, logging, evaluation, and continuous feedback mechanisms. Collaborate with platform and infrastructure teams to deploy AI models and agents ...

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 ...

value creation initiatives to be backed by reliable, real-time data. When companies partner with JMAN, we combine our data science and data engineering expertise with our deep commercial understanding to deliver tangible, high-value outcomes at pace. Founded in 2010, JMAN has a global footprint with offices … Data Science or applied machine learning, ideally in consulting, high‐growth, or commercial environments. Strong applied machine learning expertise, including: Model selection and development Feature engineering Evaluation and validation Deployment into production environments Demonstrated experience taking models beyond experimentation into operational, business‐critical systems. Strong background in statistics ...

queries and manage remediation actions and findings Your skills and experience Advanced degree (Master’s or PhD) in a quantitative discipline (e.g., Mathematics, Physics, Engineering, Computer Science) 7+ years’ experience developing analytical or machine learning models in financial services (ideally Anti-Financial Crime/Screening) Experience building Natural Language … Processing (NLP)/Machine Learning (ML)/Artificial Intelligence (AI) pipelines, including feature engineering, model training, evaluation and monitoring Experience designing evaluation frameworks for LLM use cases (e.g., human-in-the-loop, calibration, guardrails, auditability) Experience with hyperparameter tuning at scale and experimentation frameworks Strong technical experience with ...

machine learning techniques including regression, classification, clustering, optimisation and ensemble methods. Experience delivering end-to-end data science solutions, from data acquisition and feature engineering through to model deployment and monitoring. Strong understanding of model governance in a commercial environment. Ability to lead technical discussions, influence stakeholders ...

platform design. Ensure architectures meet security, privacy, and regulatory requirements, including GDPR and data residency considerations. Review solution designs, provide architectural assurance, and support engineering teams through design authority and peer review. Design data architectures that enable AI adoption — ensuring data is discoverable, well-governed, and AI-ready … machine learning, GenAI, and agentic use cases across the business. Partner with AI/ML teams to define patterns for feature engineering, model data pipelines, vector search, and safe exposure of enterprise data to AI systems. Evaluate emerging technologies and make pragmatic build/buy/adopt recommendations ...

learning models focused on user personalisation, including recommendation engines, ranking algorithms, user segmentation and content analysis. Construct and maintain robust, scalable data pipelines for feature engineering and model training, handling both structured and unstructured large‐scale datasets. Deploy and supervise ML models in production, ensuring high availability, optimal ...

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. ...

advanced ranking models for Depop’s entire app, covering personalised recommendations, search results and other surfaces. 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 ranking by researching and integrating emerging ML techniques, frameworks, and tooling, while contributing technical expertise to long‐term product and data strategy. Mentor, coach, and set technical direction within the Ranking ML team ...

Operations leadership and the technical team. Requirements Technical Foundation: You possess a strong foundational understanding of machine learning—whether gained through professional PM experience, engineering roles, or dedicated personal projects. We value fast learners who can grasp the mechanics of model training and feature engineering … quickly when new fraud trends emerge. Communication: Ability to explain complex ML logic to non-technical stakeholders while maintaining deep technical credibility with your Engineering partners. Bonus points Technical Background: A background as Data Analyst, Engineer, or ML Scientist is a big plus! Domain Expertise: Direct experience in Trust ...

domains involving high-frequency or large-scale time-series data. You will focus on developing alpha signal generation pipelines from data ingestion and feature engineering to model training and deployment - in collaboration with our trading and infrastructure teams. Responsibilities Develop ML-based alpha generation models using high-frequency … order book and market microstructure data. Design and maintain data pipelines, preprocessing, and feature extraction workflows tailored to streaming tick data. Research and implement advanced deep learning architectures for short-horizon forecasting and signal extraction. Collaborate with quant researchers and developers to integrate models into live trading environments. Optimise ...

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 ...

Fine-tune, evaluate, and monitor LLM performance for business use cases. Integrate AI solutions with enterprise applications, APIs, databases, and cloud platforms. Implement prompt engineering strategies to improve response quality and accuracy. Develop model guardrails, content filtering, and responsible AI controls. Conduct AI model testing, benchmarking, and performance optimization. … Hugging Face Vector Databases (Pinecone, FAISS, ChromaDB, Weaviate, Milvus) Machine Learning fundamentals. NLP concepts and transformer architectures. Model evaluation and observability. Data preprocessing and feature engineering. We are a Disability Confident Employer: Capgemini is proud to be a Disability Confident Employer (Level 2) under the UK Government’s Disability ...