5 of 5 Remote/Hybrid Random Forest Jobs in the UK excluding London

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 ...

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 ...

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. ...