14 of 14 Random Forest Jobs in Manchester

tolerant code using Python and SQL, and champion best practices like code reviews, pair programming, and knowledge- sharing sessions. Apply robust ML and statistical techniques ( from classical models like Random Forest to state- of- the- art NLP and LLMs) to solve complex problems across multiple domains. Collaborate closely with stakeholders, ensuring technical solutions are well- communicated and drive More ❯

requirements. Key Skills and Experience: Previous experience within general insurance 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 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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯