6 of 6 Permanent Random Forest Jobs in the South East

above). Strong numerical background with a knowledge of key statistical principals e.g. Bayesian and frequentist statistics, probability distributions. Fundamental understanding of ML algorithms e.g. linear and logistic regression, random forest, neural networks, time series models. Experience with multiple programming languages, with a preference for SQL and Python. Familiarity with large language models and prompt engineering would be More ❯

above). Strong numerical background with a knowledge of key statistical principals e.g. Bayesian and frequentist statistics, probability distributions. Fundamental understanding of ML algorithms e.g. linear and logistic regression, random forest, neural networks, time series models. Experience with multiple programming languages, with a preference for SQL and Python. Familiarity with large language models and prompt engineering would be More ❯

AI, or a related discipline. Strong numerical and statistical knowledge (e.g. Bayesian/frequentist methods, probability distributions). Solid understanding of machine learning algorithms (e.g. linear/logistic regression, random forests, neural networks, time series models). Experience with multiple programming languages — with Python and SQL preferred. Interest or familiarity with large language models and prompt engineering (desirable). More ❯

Skills and Experience: Previous experience within Personal Lines Pricing is advantageous 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 good quantitative degree (Mathematics, Statistics, Engineering, Physics, Computer Science More ❯

Skills and Experience: Previous experience within Personal Lines Pricing is advantageous 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 good quantitative degree (Mathematics, Statistics, Engineering, Physics, Computer Science More ❯

Manage the full model development lifecycle, including data preparation, exploratory analysis, model training, validation, and deployment. Develop and fine-tune predictive algorithms such as: Classification: Logistic Regression, Decision Trees, Random Forests. Regression: Linear Models, Gradient Boosting, Neural Networks, K-Nearest Neighbors. Build models for customer behavior prediction and risk assessment in insurance, particularly in underwriting and claims. Apply natural More ❯