6 of 6 Random Forest Jobs in the UK excluding London

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 More ❯

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 More ❯

team’s modelling capabilities SKILLS AND EXPERIENCE Experience developing credit risk models using logistic regression or similar Knowledge of machine learning approaches such as random forest and clustering Proficient in Python Educated to at least university level with a STEM degree THE BENEFITS £32,000+ base salary Discretionary More ❯

e.g., SAS) to manipulate data. Experience with predictive modelling techniques such as Logistic Regression, Log-Gamma GLMs, GBMs, Elastic Net GLMs, GAMs, Decision Trees, Random Forests, Support Vector Machines, and Neural Nets. Skilled in programming languages (e.g., R, Matlab, Python, or Octave). Knowledge and/or experience in More ❯

commercial thinking Collaborating closely with marketing, BMD, and product teams to deliver analytics that support business priorities Building advanced models using techniques such as random forest , linear regression , and k-means clustering Translating analytical findings into clear, actionable recommendations for senior stakeholders Driving continuous improvement in data quality More ❯

estimation/tracking algorithms e.g. Kalman Filtering, multiple-model tracking methods, particle filters, grid-based estimation methods, Multi-Object-Multi-Sensor Fusion, data-association, random finite sets, Bayesian belief networks, Dempster-Shafer theory of evidence Machine Learning for regression and pattern recognition/discovery problems e.g. Gaussian processes, latent … variable methods, support vector machines, probabilistic/statistical models, neural networks, Bayesian inference, random-forests, novelty detection, clustering Deep Learning e.g. Deep reinforcement learning, Monte-Carlo tree search, deep regression/classification, deep embeddings, recurrent Networks, natural language processing Computer Vision algorithms e.g. Structure from motion, image Based navigation More ❯