17 of 17 Random Forest Jobs in the UK

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 ❯

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 ❯

add or enhance data sets that deliver incremental value to New Look. Advanced Analytics: Experience in delivering and measuring data science projects such as random forest, k-means, linear regression. Optimization: Collaborate with teams to identify process optimization opportunities. Collaborate: Lead an analytical principle and deliver an analytics … analysts Proven experience leading analytics projects from conception to implementation, with strong project planning and management skills Strong understanding of analytical modelling techniques (e.g., random forest, k-means, linear regression) Excellent communication and presentation skills Accurate and relevant analytical insights Focus on Data Quality Improvement Promote Adoption Rate 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 ❯

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 ❯

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) A good understanding of the regulatory environment, especially responsible lending (creditworthiness/affordability) Experience in using the latest data 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 ❯

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 ❯

science, modelling, or analytics. Proven expertise in Python/SQL coding and credit risk modelling. Advanced knowledge of machine learning techniques (e.g., deep learning, random forests, clustering, anomaly detection). Strong understanding of Credit bureau data. Hands-on experience in data pipelines, ML Ops, and building scalable solutions. 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 ❯

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 ❯

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 ❯

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 ❯