7 of 7 Remote Random Forest Jobs in the UK

understanding of computer science fundamentals, including data structures, algorithms, data modelling, and software architecture Solid understanding of classical Machine Learning algorithms (e.g., Logistic Regression, Random Forest, XGBoost, etc.), state-of-the-art research areas (e.g., NLP, Transfer Learning, etc.), and modern Deep Learning algorithms (e.g., BERT, LSTM, etc. More ❯

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 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 ❯

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 More ❯

Skills and Experience Expert level knowledge of data science and machine learning, including a range of different techniques such as supervised (e.g. decision trees, random forests), unsupervised (e.g. clustering), and deep learning. Knowledge of generative AI is desirable. Expert level of knowledge of statistics, applied mathematics and scientific analysis More ❯

Skills and Experience Expert level knowledge of data science and machine learning, including a range of different techniques such as supervised (e.g. decision trees, random forests), unsupervised (e.g. clustering), and deep learning. Knowledge of generative AI is desirable. Expert level of knowledge of statistics, applied mathematics and scientific analysis 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 ❯