10 of 10 Remote/Hybrid Feature Engineering Jobs in London

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 ...

solutions Ideal but Not Essential We'd particularly like to hear from candidates who have exposure to: Statistics, machine learning, or predictive modelling Data engineering and feature engineering concepts Pricing, economics, or commercial analytics (e.g. willingness to pay, price elasticity, share of wallet) Stakeholder management and storytelling … Need? A Bachelor's degree in a technical or analytical subject such as: Data Analytics Data Science Computer Science Mathematics Statistics Economics Business Analytics Engineering Or a related discipline You'll thrive in this role if you: Enjoy working with data to answer questions and solve problems Are naturally ...

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 ...

concept into production environments Develop scalable ML pipelines using AWS cloud technologies Implement MLOps and LLMOps best practices for deployment, monitoring and governance Support feature engineering, model optimisation and performance monitoring Apply responsible AI principles, including model explainability and fairness Present findings and recommendations to technical … tools such as MLflow, Weights & Biases or Data Version Control Experience designing, testing and evaluating machine learning experiments Knowledge of Generative AI, LLMs, prompt engineering and RAG architectures Experience with LLMOps tools such as LangChain, LangSmith or LangGraph Strong understanding of model monitoring, validation and governance Excellent communication ...

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 ...

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 ...

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 ...