13 of 13 Random Forest Jobs in the UK

machine learning services) Timeseries forecasting Solid understanding of computer science fundamentals, including data structures, algorithms, data modelling and software architecture Strong knowledge of Machine Learning algorithms (e.g. Logistic Regression, Random Forest, XGBoost, etc.) as well as state-of-the-art research area (e.g. NLP, Transfer Learning etc.) and modern Deep Learning algorithms (e.g. BERT, LSTM, etc.) Machine Learning More ❯

and experience in exploratory data analysis, inferential statistics, and machine learning, including: Clustering techniques (e.g., k-medoids, hierarchical clustering) Predictive modelling (e.g. Classification and Regression Trees (CART), Linear Regression, Random Forest, Gradient Boosted Models) Natural Language Processing (NLP) with a focus on social listening and topic modelling Integration of generative AI and LLMs in qualitative and survey research More ❯

AI, Computational Actuarial Science, Data Science Analysing, cleaning and processing historical data Processing large and complex volumes of data Researching models (such as GLMs, regression trees, random forest, etc) using historic data relevant to insurance claim prediction Testing models and evaluating performance across various algorithms, using company data Developing research and commercial objectives for the company related to More ❯

for clients. Create segmentation models using multinomial logistic regression and linear discriminant analysis. Advanced Analytics Skills Strong working knowledge of analytical techniques such as conjoint analysis, machine learning (e.g., Random Forests, SVM), statistical methods (e.g., regression), time series, basket analysis, and unstructured data analytics. Ability to synthesize multiple data sources into meaningful insights and actionable business metrics. Knowledge of 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 ❯

knowledge of current trends and issues in motor or home 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. Knowledge of the technical differences between different packages for some of these model types would be an advantage. Experience in statistical and data science More ❯

knowledge of current trends and issues in motor or home 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. Knowledge of the technical differences between different packages for some of these model types would be an advantage. Experience in statistical and data science More ❯

Previous experience within data science Experience in commercial pricing and modelling, ideally with a focus in Motor Experience and detailed technical knowledge of GLMs/Elastic Nets, GBMs, GAMs, Random Forests, and clustering techniques Experience in programming languages (e.g. Python, PySpark, R, SAS, SQL) Proficient at communicating results in a concise manner both verbally and written Behaviours: Motivated by More ❯

and/or specialized courses are an asset, especially in Data Science, Quantitative Finance or similar. Should desirably have knowledge of modeling techniques (logit, GLM, time series, decision trees, random forests, clustering), statistical programming languages (SAS, R, Python, Matlab) and big data tools and platforms (Hadoop, Hive, etc.). Solid academic record. Strong computer skills. Knowledge of other languages More ❯

and/or specialized courses are an asset, especially in Data Science, Quantitative Finance or similar. Should desirably have knowledge of modeling techniques (logit, GLM, time series, decision trees, random forests, clustering), statistical programming languages (SAS, R, Python, Matlab) and big data tools and platforms (Hadoop, Hive, etc.). Solid academic record. Strong computer skills. Knowledge of other languages More ❯

and/or specialized courses are an asset, especially in Data Science, Quantitative Finance or similar. Should desirably have knowledge of modeling techniques (logit, GLM, time series, decision trees, random forests, clustering), statistical programming languages (SAS, R, Python, Matlab) and big data tools and platforms (Hadoop, Hive, etc.). Solid academic record. Strong computer skills. Knowledge of other languages More ❯

and/or specialized courses are an asset, especially in Data Science, Quantitative Finance or similar. Should desirably have knowledge of modeling techniques (logit, GLM, time series, decision trees, random forests, clustering), statistical programming languages (SAS, R, Python, Matlab) and big data tools and platforms (Hadoop, Hive, etc.). Solid academic record. Strong computer skills. Knowledge of other languages More ❯

and/or specialized courses are an asset, especially in Data Science, Quantitative Finance or similar. Should desirably have knowledge of modeling techniques (logit, GLM, time series, decision trees, random forests, clustering), statistical programming languages (SAS, R, Python, Matlab) and big data tools and platforms (Hadoop, Hive, etc.). Solid academic record. Strong computer skills. Knowledge of other languages More ❯