6 of 6 Permanent Random Forest Jobs in the UK

regression, ranking) Analyse financial, credit, and time-series data to extract predictive signals Build and benchmark models such as: Gradient boosting (XGBoost, LightGBM, CatBoost) Random Forests Logistic/linear regression Design LLM-powered workflows for: Document understanding (loan docs, credit memos, financials) Risk factor extraction Deal-level reasoning ...

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 ...

tuning and deploying machine learning methods 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 DevOps and Azure ML, or other MLOps and ML Lifecycle technology stacks, such as AWS, Databricks, Google ...

tailored to the needs of varied audiences. Expertise in the application of several supervised and unsupervised machine learning techniques such as classification (e.g. Random Forests, Support Vector Machine), regression (e.g. Ridge Regression, Lasso), dimensionality reduction (e.g. Principal Components Analysis, t-SNE) and clustering (e.g. KMeans, Hierarchical Clustering). Desirable ...

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 ...

experience Previous experience within data science Experience in insurance pricing and modelling Experience and detailed technical knowledge of GLMs/Elastic Nets, GBMs, GAMs, Random Forests, Neural Networks and clustering techniques Knowledge of statistics and distributions commonly used in insurance Experience in programming languages (e.g. Python, PySpark ...