11 of 11 Random Forest Jobs in England

skills using tools like Tableau, Spotfire, Power BI etc. Expertise in predictive modelling, including both parametric (e.g. logit/probit) and non-parametric (e.g. random forest, neural net) techniques as well as wider ML techniques like clustering/random forest (desirable). Tech Stack: SQL, Python ...

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 ...

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 ...

stakeholders contributing project components of a larger Data Science oriented programme of work. Knowledge in key Machine Learning based algorithms: Supervised (Linear regression, SVM, Random Forests, Gradient Boosting and Neural Networks) and Unsupervised (KMeans, Gaussian Mixtures) and the entire end to end ML pipeline. Experience of managing NLP problems ...

developing cloud-based machine learning services using one or more cloud providers (preferably GCP) Excellent understanding of classical Machine Learning algorithms (e.g. Logistic Regression, Random Forest, XGBoost, etc.) and modern Deep Learning algorithms (e.g. BERT, LSTM, etc.) < Strong knowledge of SQL and Python's ecosystem for data analysis ...

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 ...

partnerships Apply traditional model development techniques and approaches (e.g. logistic regression) as well as adopting new AI/ML techniques such as gradient boosting, random forests, clustering, etc. Utilise the latest cloud based technologies and analytical tooling to support test-and-learn champion/challenger approaches towards new fraud ...

Demonstrated ability to write clean, efficient code. Proficiency in Python or C# Familiarity with standard ML algorithms (e.g., linear/logistic regression, decision trees, random forests) and libraries like Scikit-Learn or TensorFlow/PyTorch Demonstrable experience in being able to quickly learn and adapt to new areas ...

commercial environment.Deep Modelling Expertise: Expert-level knowledge of statistical analysis, predictive modelling, and a variety of ensemble techniques (e.g., Gradient Boosted Trees, Random Forests).Core Technical Proficiency: High proficiency in Python for data science (including libraries like Pandas, Scikit-Learn, XGBoost/LightGBM) and SQL.Large-Scale Data Expertise: Demonstrable ...

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 ...

Experience in financial markets, algo-trading, or prediction market platforms (Polymarket, Manifold, etc.) Quantitative background in maths, statistics, or probability theory ML experience including random forests, regression models, scikit-learn, and PyTorch AWS infrastructure experience deploying containerised applications and managing cloud environments (EC2, Lambda, RDS) Open-source contributions ...