26 to 50 of 59 Random Forest Jobs in England

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 ❯

customer satisfaction Requirements: MSc or PhD Degree in Computer Science, Artificial Intelligence, Mathematics, Statistics or related fields. Expert in Python, R, SQL and a range of ML techniques (e.g., random forests, neural nets, reinforcement learning) Track record of delivering high-impact AI projects from concept to production Strong communication skills – able to translate complex insights into business value Passionate 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 ❯

e.g. Power BI). Microsoft suite - Excel, Powerpoint etc. Languages: Python, SQL, Git. Working towards more complex modelling of the data using innovative methods. Knowledge of Machine Learning: Classification (Random Forest, Decision Trees, KNN), Regression. Modelling (linear, sparse, logistic), Principal Component Analysis (PCA, PCR), clustering (K-means). Profile: Strong analytical skills, ability to generate strong insights and More ❯

e.g. Power BI). Microsoft suite - Excel, Powerpoint etc. Languages: Python, SQL, Git. Working towards more complex modelling of the data using innovative methods. Knowledge of Machine Learning: Classification (Random Forest, Decision Trees, KNN), Regression. Modelling (linear, sparse, logistic), Principal Component Analysis (PCA, PCR), clustering (K-means). Profile: Strong analytical skills, ability to generate strong insights and More ❯

enterprise ML products. Expertise in Mathematical Optimization: linear programming (LP), quadratic programming (QP), and mixed-integer programming (MIP). Proficiency in Machine Learning models, including time-series forecasting, clustering, random forest, and deep learning. Hands-on experience with solvers like Gurobi, CPLEX, SCIP, or GLPK. Familiarity with Agile methodologies such as Scrum or Kanban. Preferred Skills Strategic thinking More ❯

Experience Substantial experience within Personal Lines Pricing, ideally including team or project leadership Proficiency in predictive modelling techniques such as Logistic Regression, GBMs, Elastic Net GLMs, GAMs, Decision Trees, Random Forests, Neural Nets, and Clustering Strong skills in R, Python, PySpark, SAS, or SQL Proven ability to interpret performance data and make commercial recommendations Experience with WTW's Radar More ❯

/or statistical packages e.g. actuarial pricing software Experience in SOME of the following predictive modelling techniques e.g.Logistic Regression, Log-Gamma GLMs, GBMs, Elastic Net GLMs, GAMs, Decision Trees, Random Forests, Support Vector Machines and Neural Nets Experienced in the use of a programming language (e.g. R, Matlab, Python or Octave) Experience of using Emblem and Radar Highly numerate More ❯

Y being predicted and independent variable X1, X2, X3..........Xn . Here x is a predictor and Y is function of X variables. The simple equation of linear regression is- Random forest regression is a bagging technique where the parts of the main dataset get distributed among multiple Decision Trees that will predict the best model. And finally based … on the root mean square error(RMSE), it will aggregate the best model or choose the best predictive model. In Random Forest Process, we have some base learner models like M1, M2, M3 .. Mn . These base learner model are called Decision Trees . Each decision tree will randomly pickup the number of rows and columns from … model. When you create an experiment, Automated ML will create multiple models for you. Based on the normalized root mean squared error , we will select our best model i.e. Random Forest and deploy as a web service Here you need to provide some details like name of the model and compute type Click on Deploy Note : If we More ❯

ongoing don't miss your chance to secure the future of your career! Contact me @ (url removed) or on (phone number removed). Data Science, Data Scientist, AI, ML, Random Forest, Databricks, SageMaker, Regression, Gradient Boosting, NLP, Palantir, Insurance, Banking More ❯

working to support the senior data modeller in creating, developing, and iteratively improving models based on historic trends. The models will vary in design, but current models include XGboost, Random Forest and MCMC models. This role will also create analysis and provide support to the pricing team, to increase their automation and accuracy of analysis in the financial … that benefit both customers and clients. The Person Experience of using machine learning techniques on real data is needed for this role. Desirable if the candidate has experience of random forests, XGboosts or Monte Carlo. Proficiency in Python for data manipulation and model development. Otherwise, proficiency in another language (such as R, Java etc) with the ability to learn More ❯

and understanding of statistical methods such as time to event analysis, machine learning, meta-analysis, mixed effect modeling, longitudinal modeling, Bayesian methods, variable selection methods (e.g., lasso, elastic net, random forest), design of clinical trials. Strong programming skills in R and Python. Demonstrated knowledge of data visualization, exploratory analysis, and predictive modeling. Excellent interpersonal and communication skills (verbal 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 ❯

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 ❯

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 ❯

Experience Substantial experience within Personal Lines Pricing, ideally including team or project leadership Proficiency in predictive modelling techniques such as Logistic Regression, GBMs, Elastic Net GLMs, GAMs, Decision Trees, Random Forests, Neural Nets, and Clustering Strong skills in R, Python, PySpark, SAS, or SQL Proven ability to interpret performance data and make commercial recommendations Experience with WTW’s Radar More ❯

Experience Substantial experience within Personal Lines Pricing, ideally including team or project leadership Proficiency in predictive modelling techniques such as Logistic Regression, GBMs, Elastic Net GLMs, GAMs, Decision Trees, Random Forests, Neural Nets, and Clustering Strong skills in R, Python, PySpark, SAS, or SQL Proven ability to interpret performance data and make commercial recommendations Experience with WTW's Radar More ❯

data science processes (e.g. pipelines) and associated technologies Exposure to range of risk modelling techniques (e.g. logistic and time series regressions and Machine Learning methods, such as gradience boosting, random forests, etc.) Red Hot Rewards Generous holidays - 38.5 days annual leave (including bank holidays and prorated if part-time) plus the option to buy more. Up to five extra More ❯

data science processes (e.g. pipelines) and associated technologies Exposure to range of risk modelling techniques (e.g. logistic and time series regressions and Machine Learning methods, such as gradience boosting, random forests, etc.) Red Hot Rewards Generous holidays - 38.5 days annual leave (including bank holidays and prorated if part-time) plus the option to buy more. Up to five extra More ❯

data science processes (e.g. pipelines) and associated technologies Exposure to range of risk modelling techniques (e.g. logistic and time series regressions and Machine Learning methods, such as gradience boosting, random forests, etc.) Red Hot Rewards Generous holidays - 38.5 days annual leave (including bank holidays and prorated if part-time) plus the option to buy more. Up to five extra More ❯

data science processes (e.g. pipelines) and associated technologies Exposure to range of risk modelling techniques (e.g. logistic and time series regressions and Machine Learning methods, such as gradience boosting, random forests, etc.) Red Hot Rewards Generous holidays - 38.5 days annual leave (including bank holidays and prorated if part-time) plus the option to buy more. Up to five extra More ❯

monitoring, and scaling of models. Qualifications Proven experience as a Machine Learning Engineer, with leadership experience in deploying models in production. Experience with classical ML algorithms (e.g., Logistic Regression, Random Forest, XGBoost), NLP, Transfer Learning, Deep Learning (e.g., BERT, Llama, LLMs). Expertise in end-to-end ML development and applications involving LLMs and frameworks like Langchain. Familiarity More ❯

discipline such as Computer Science, Statistics, Operational Research or Engineering • Experience working in Analytics/Business Intelligence environment • Experience/knowledge of advanced machine learning techniques such as GBM, random forest, etc. Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during More ❯

clinical trial exposure. Strong knowledge of statistical methods like time-to-event analysis, machine learning, meta-analysis, mixed-effect modeling, Bayesian methods, variable selection techniques (e.g., lasso, elastic net, random forest), and clinical trial design. Proficiency in R and Python, with experience in data visualization, exploratory analysis, and predictive modeling. Excellent communication skills, both verbal and written. Ability More ❯

and understanding of statistical methods such as time to event analysis, machine learning, meta-analysis, mixed effect modeling, longitudinal modeling, Bayesian methods, variable selection methods (e.g., lasso, elastic net, random forest), design of clinical trials. Strong programming skills in R and Python. Demonstrated knowledge of data visualization, exploratory analysis, and predictive modeling. Excellent interpersonal and communication skills (verbal More ❯