25 of 25 Permanent Random Forest Jobs in England

data science lifecycle across large, multi-source datasets, from analysis and cleansing through to deployment. Apply a broad statistical toolkit, from GLMs and random forest to NLP, clustering and scenario analysis, to generate insight that gets acted on. est and develop new external data sources to keep … modelling and machine learning. Hands-on experience building and evaluating machine learning models. Confident statistical modelling skills across techniques such as GLMs, decision trees, random forest and NLP. Working knowledge of SQL, data structures, ETL and data integration. A degree in Mathematics, Statistics, Data Science or another quantitative ...

dash, D3.js, or QlikSense Experience in SQL (any variant), MS Excel and VBA Experience in applying various analytical techniques (e.g. naïve bayes, decision trees, random forest, xgboost and k-means), to calculate problems such as predictive forecasting, prescriptive action, capacity planning. Experience of working in teams, managing ...

plotly-dash, D3.js, or QlikSenseExperience in SQL (any variant), MS Excel and VBAExperience in applying various analytical techniques (e.g. na ve bayes, decision trees, random forest, xgboost and k-means), to calculate problems such as predictive forecasting, prescriptive action, capacity planning. Experience of working in teams, managing ...

techniques, e.g. skilled in linear algebra. Experience conducting hands-on analytics projects using advanced statistical methods such as generalized regression models, Bayesian methods, random forest, gradient boosting, neural networks, machine learning, clustering, or similar methodologies. Proficiency in programming languages such as Python, Sql etc. Experience with Data visualization ...

project components of a larger Data Science oriented programme of work. Desirable criteria Knowledge in key Machine Learning based algorithms: Supervised (Linear regression, SVM, Random Forests, Gradient Boosting and Neural Networks) and Unsupervised (KMeans, Gaussian Mixtures) and the entire end to end ML pipeline. Certification in DevOps, Agile ...

similar tools. Experience with advanced algorithms in deep learning, neural networks, reinforcement learning, and transfer learning. Familiarity with traditional machine learning algorithms such as random forests, SVM, logistic regression, and Bayesian modelling for model building, validation, and testing. Familiarity with cloud platforms (e.g., Bedrock, AWS, Azure) for model deployment ...

quantitative discipline such as Computer Science, Statistics, Mathematics, Economics, or similar Working knowledge of statistical and machine learning methods (e.g. logistic regression, gradient boosting, random forests, clustering, NLP Proficient in Python and/or R, with strong experience in data quality, model development and feature engineering Strong command ...

quantitative discipline such as Computer Science, Statistics, Mathematics, Economics, or similar Working knowledge of statistical and machine learning methods (e.g. logistic regression, gradient boosting, random forests, clustering, NLP Proficient in Python and/or R, with strong experience in data quality, model development and feature engineering Strong command ...

models R&D projects Requirements: Recent graduates or final year students. Should desirably have knowledge of Artificial Intelligence modeling techniques (logit, GLM, time series, random forests, clustering, XGBoost), statistical programming languages (SAS, R, Python, Matlab), big data tools and platforms (Hadoop, Hive, etc.) and cloud platforms (AWS, Azure ...

insights from data, ideally within healthcare, insurance or another regulated industry. Hands-on expertise with supervised and unsupervised machine learning techniques, including: XGBoost LightGBM Random Forests Clustering techniques Regression analysis Correlation analysis Multilevel modelling Strong programming and data engineering skills using Python, SQL and/or SAS. Experience building ...

management skills. Experience & Knowledge Proven experience in data science and data analysis. Good understanding of statistical techniques and their application, including GLMs, decision trees, random forest, boosting, natural language processing, distributions, clustering, simulation and scenario analysis. Knowledge of data platforms and data processing, including batch and real‐time ...

data, analytics and automation Experience integrating REST APIs and multiple data sources Knowledge of traditional machine learning techniques, including tree-based models (e.g. Random Forest, CatBoost), feature engineering techniques such as TF-IDF, and experience evaluating and deploying AI/ML solutions in commercial environments Excellent problem-solving ...

cloud‐based machine learning services using one or more cloud providers (preferably GCP). Excellent understanding of classical Machine Learning algorithms (e.g. Logistic Regression, Random Forest, XGBoost, etc.) and modern Deep Learning algorithms (e.g. BERT, LSTM, etc.). Strong knowledge of SQL and Python’s ecosystem for data ...

developing cloud-based machine learning services using one or more cloud providers (preferably GCP) Excellent understanding of classical Machine Learning algorithms (e.g. Logistic Regression, Random Forest, XGBoost, etc.) and modern Deep Learning algorithms (e.g. BERT, LSTM, etc.) < Strong knowledge of SQL and Python's ecosystem for data analysis ...

developing cloud-based machine learning services using one or more cloud providers (preferably GCP) Excellent understanding of classical Machine Learning algorithms (e.g. Logistic Regression, Random Forest, XGBoost, etc.) and modern Deep Learning algorithms (e.g. BERT, LSTM, etc.) Strong knowledge of SQL and Python's ecosystem for data analysis ...

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 ...

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 quantitative degree (Mathematics, Statistics ...

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 quantitative degree (Mathematics, Statistics ...

optimising of machine learning models for fraud detection and financial crime use cases. Fraud detection model development experience using techniques such as gradient boosting, random forests, neural networks, ensemble models and graph-based analytics. Model evaluation, performance tuning and explainability experience. Data science and statistical modelling background, including hypothesis ...

research code into production code. Hands on experience in machine learning engineering, including deploying, monitoring, and maintaining ML models in production environments (Neural networks, Random forests etc.). Experience in financial services or insurance is an advantage but not required. Solid experience as a Python developer, ideally ...

logistic or Poisson, multilevel models, Analysis of Variance. Bayesian approaches to modelling Causal inference Epidemiology Machine Learning/predictive models: regression/classification trees, random forests, boosting, neural networks Experimental design and applying the appropriate testing framework to analytics. Observational study design using real world data Expert in open ...

recommendations Apply statistical and advanced analytical techniques including regression, clustering, predictive modelling and campaign measurement Have an understanding of techniques such as k-means, random forest and linear regression, with the ability to interpret and utilise modelling outputs Support forecasting and optimisation activity across the wider analytics function ...

Experience in financial markets, algo-trading, or prediction market platforms (Polymarket, Manifold, etc.) Quantitative background in maths, statistics, or probability theory ML experience including random forests, regression models, scikit-learn, and PyTorch AWS infrastructure experience deploying containerised applications and managing cloud environments (EC2, Lambda, RDS) Open-source contributions ...

Poisson, multilevel models, Analysis of Variance. o Bayesian approaches to modelling o Causal inference o Machine Learning/predictive models: regression/classification trees, random forests, boosting, neural networks Experimental design and applying the appropriate testing framework to analytics. Experience improving decision quality, working with system leaders ...

use. We’ll rely on your in-depth knowledge of the ML ecosystem and understanding of varying approaches - whether it’s neural networks, random forests, gradient-boosted trees or sophisticated ensemble methods - to aid decision-making so we apply the right tool for the problem at hand. Your work ...