7 of 7 Random Forest Jobs in the UK

Data Scientist or Operational Research focused role working with large, complex datasets Solid understanding of machine learning techniques such as gradient boosting, random forests and time series modelling Experience building and deploying models used in real world decision making (e.g. ensemble methods, regularised regression) Advanced proficiency in R, including ...

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 ...

Expertise: Solid understanding of computer science fundamentals and time-series forecasting. Machine Learning: Strong grasp of ML and deep learning algorithms (e.g. Logistic Regression, Random Forest, XGBoost, BERT, LSTM, NLP, Transfer Learning). Reasonable Adjustments: Respect and equality are core values to us. We are proud ...

continuous refinement of predictive and probabilistic models based on historic and real-time trends. You will guide the implementation of advanced techniques including XGBoost , Random Forest, and MCMC , ensuring model robustness, scalability, and business relevance. You will act as a strategic partner to the pricing and operational teams … applying machine learning techniques to real-world datasets, with demonstrable outcomes in business decisioning or operational optimisation. Expertise in advanced modelling approaches such as Random Forests, XGBoost, and Monte Carlo simulations. Proficiency in Python for data manipulation, model development, and deployment. Experience with other languages (e.g. R, Java, Scala ...

issues in motor or home pricing. Experience with some of the following predictive modelling techniques; GLMs, GBMs, Elastic Net GLMs, GAMs, Decision Trees, Random Forests, Neural Nets and Clustering, with knowledge of the technical differences between different packages for some of these model types would be an advantage. Experience ...

Experience: Previous experience in tuning and deploying machine learning methods Experience of the following predictive modelling techniques; GBMs, Elastic Net GLMs, GAMs, Logistic Regression, Random Forests, Decision Trees, Neural Nets and Clustering Experience in DevOps, or other MLOps and ML Lifecycle stacks Experience with Docker and Kubernetes Experience ...

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 ...