14 of 14 Permanent Random Forest Jobs in the UK excluding London

Jobs) and workflow orchestration with Apache Airflow. Strong working knowledge of both supervised and unsupervised techniques, including tree-based and kernel methods (gradient boosting, random forest, SVM) and anomaly-detection approaches such as Isolation Forest, Local Outlier Factor, and locality-sensitive hashing. Solid grounding in data ...

Jobs) and workflow orchestration with Apache Airflow. Strong working knowledge of both supervised and unsupervised techniques, including tree-based and kernel methods (gradient boosting, random forest, SVM) and anomaly-detection approaches such as Isolation Forest, Local Outlier Factor, and locality-sensitive hashing. Solid grounding in data ...

experience with machine learning libraries such as scikit-learn, TensorFlow, or PyTorch. Practical knowledge of algorithms and techniques such as regression, decision trees, random forest, XGBoost, k-means, anomaly detection, and time series forecasting. Working understanding of model evaluation and explainability concepts, e.g. overfitting, bias, cross-validation. Experience ...

quantitative discipline such as Computer Science, Statistics, Mathematics, Economics, or similar Working knowledge of statistical and machine learning methods (e.g. logistic regression, gradient boosting, random forests, clustering, NLP Proficient in Python and/or R, with strong experience in data quality, model development and feature engineering Strong command ...

management skills. Experience & Knowledge Proven experience in data science and data analysis. Good understanding of statistical techniques and their application, including GLMs, decision trees, random forest, boosting, natural language processing, distributions, clustering, simulation and scenario analysis. Knowledge of data platforms and data processing, including batch and real‐time ...

Previous experience within general insurance pricing Experience with some of the following predictive modelling techniques; Logistic Regression, GBMs, Elastic Net GLMs, GAMs, Decision Trees, Random Forests, Neural Nets and Clustering Experience in statistical and data science programming languages (e.g. R, Python, PySpark, SAS, SQL) A quantitative degree (Mathematics, Statistics ...

recommendations Apply statistical and advanced analytical techniques including regression, clustering, predictive modelling and campaign measurement Have an understanding of techniques such as k-means, random forest and linear regression, with the ability to interpret and utilise modelling outputs Support forecasting and optimisation activity across the wider analytics function ...

stakeholder management and communication skills, with the ability to influence non-technical audiences. Exposure to analytical techniques such as k-means clustering, regression modelling, random forest, or similar methods. Experience leading projects and managing multiple priorities in a commercial environment. Previous mentoring or line management experience would ...

research code into production code. Hands on experience in machine learning engineering, including deploying, monitoring, and maintaining ML models in production environments (Neural networks, Random forests etc.). Experience in financial services or insurance is an advantage but not required. Solid experience as a Python developer, ideally ...

Expertise: Solid understanding of computer science fundamentals and time-series forecasting Machine Learning: Strong grasp of ML and deep learning algorithms (e.g. Logistic Regression, Random Forest, XGBoost, BERT, LSTM, NLP, Transfer Learning) Machine Learning Engineer, XGBoost, GCP, Python, SQL, time-series forecasting Reasonable Adjustments: Respect and equality ...

Advanced SQL skills Strong data visualisation experience Experience delivering segmentation, clustering, predictive modelling and campaign measurement Understanding of techniques such as k-means clustering, random forest and regression analysis Excellent stakeholder engagement and communication skills Ability to translate analytical findings into business recommendations Previous people management or mentoring ...

retention, loyalty, CLV or profitability Comfortable applying advanced analytical techniques such as clustering, regression and predictive modelling Experience with techniques such as k-means, random forest or linear regression would be beneficial Strong commercial and stakeholder management skills, with the ability to explain complex analysis clearly ...

visualisation skills, as well as hands-on experience with advanced techniques such as segmentation, clustering, regression and predictive modelling (for example k-means and random forest). Strong stakeholder communication is essential, as this role involves influencing senior teams across the business. Line management experience would ...

plantation mapping by region – palm oil, cocoa, coffee, rubber, soy, timber and similar – in support of EUDR compliance and supply‐chain due diligence. Forest degradation and biomass/canopy‐height estimation from multi‐sensor fusion. Develop ARR feasibility models that fuse climate, soil, and remote‐sensing inputs to estimate … evaluating, and debugging ML models across the modern Python stack – deep learning (CNNs, U‐Nets, vision transformers) using PyTorch, and classical methods (gradient boosting, random forests) with scikit‐learn. Demonstrable experience with remote sensing data (optical, SAR) and an understanding of the sensor‐specific quirks that matter for modelling. ...