19 of 19 Random Forest Jobs in England

skills using tools like Tableau, Spotfire, Power BI etc. Expertise in predictive modelling, including both parametric (e.g. logit/probit) and non-parametric (e.g. random forest, neural net) techniques as well as wider ML techniques like clustering/random forest (desirable). Tech Stack: SQL, Python More ❯

skills using tools like Tableau, Spotfire, Power BI etc. Expertise in predictive modelling, including both parametric (e.g. logit/probit) and non-parametric (e.g. random forest, neural net) techniques as well as wider ML techniques like clustering/random forest (desirable). Tech Stack: SQL, Python More ❯

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 ❯

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 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 ❯

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 ❯

and specific architectures (Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and attention-based networks) Strong understanding and experience with machine learning algorithms (such as random forests, SVMs, boosting, and neural networks) along with optimization techniques. Proficiency in statistical analysis and data handling, including data augmentation, data cleaning, normalization, and 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 ❯

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 ❯

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 ❯

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 ❯

3rd party products. Key Skills & Experience: Expert in data science and machine learning, including a range of techniques such as supervised (e.g. decision trees, random forests), unsupervised (e.g. clustering) , and deep learning. Expert knowledge of exploratory data analysis and statistical analysis of large datasets. Solid experience in machine learning 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 ❯

Leading and mentoring a team of analytics professionals Staying up to date with industry trends and tools Delivering and evaluating data science projects (e.g. random forest, k-means, linear regression) Enhancing existing data sets through data mining Defining and delivering an agreed analytics strategy Turning complex data into 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 ❯

Leading and mentoring a team of analytics professionals Staying up to date with industry trends and tools Delivering and evaluating data science projects (e.g. random forest, k-means, linear regression) Enhancing existing data sets through data mining Defining and delivering an agreed analytics strategy Turning complex data into 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 ❯