1 to 25 of 57 Random Forest Jobs in England

pandas, NumPy, scikit-learn, PyTorch, and Hugging Face Transformers. Writing clean, modular, and testable code. Traditional Machine Learning Models: Experience with regression (linear, ridge), classification (logistic regression, decision trees, random forests), clustering (k-means, DBSCAN), and time-series forecasting (ARIMA, Prophet). Model evaluation, tuning, and deployment. Business Requirement Translation: Ability to convert business problems into data-driven solutions. More ❯

of two days a week from our Chester head office Team: Data and Analytics Reports to: Data Science Manager About Us The Forge Holiday Group encompasses Sykes Holiday Cottages, Forest Holidays, UKcaravans4hire and Bachcare in New Zealand. We unite under four core company values that serve as the foundation of everything we do: Being One Team, Owning It, Communicating … our Customers, Owners and Colleagues alike. Essential Experience: Extensive experience designing, developing and deploying machine learning and AI solutions in production environments Statistical modelling, machine learning (e.g. logistic regression, random forest, XGBoost, and modern deep learning techniques (e.g. transformers, transfer learning, reinforcement learning) Proven ability to lead technical direction across projects or domains Expertise in model validation, explainability More ❯

tolerant code using Python and SQL, and champion best practices like code reviews, pair programming, and knowledge- sharing sessions. Apply robust ML and statistical techniques ( from classical models like Random Forest to state- of- the- art NLP and LLMs) to solve complex problems across multiple domains. Collaborate closely with stakeholders, ensuring technical solutions are well- communicated and drive More ❯

the understanding of the structure of data sets. Proven and demonstrable experience with at least three of the following machine learning algorithms: neural networks, logistic regression, non-linear regression, random forests, decision trees, support vector machines, linear/non-linear optimization. Experience working with Java and Python, and strong understanding of data structures, algorithms, and software design patterns. Experience More ❯

the understanding of the structure of data sets. Proven and demonstrable experience with at least three of the following machine learning algorithms: neural networks, logistic regression, non-linear regression, random forests, decision trees, support vector machines, linear/non-linear optimization. Experience working with Java and Python, and strong understanding of data structures, algorithms, and software design patterns. Experience More ❯

the understanding of the structure of data sets. Proven and demonstrable experience with at least three of the following machine learning algorithms: neural networks, logistic regression, non-linear regression, random forests, decision trees, support vector machines, linear/non-linear optimization. Experience working with Java and Python, and strong understanding of data structures, algorithms, and software design patterns. Experience More ❯

machine learning to real-world problems. Experience translating customer requirements into quantitative models. Experience in one or more of the following technical disciplines: Natural Language Processing (NLP) ML (e.g., Random Forests, CatBoost, LXGM) Image Recognition Optical Character Recognition (OCR) Motion Capture Proficiency in Python, R, or C++, including relevant libraries (numpy, pandas, matplotlib, scikit-learn, tensorflow, etc.). Proficiency More ❯

customer satisfaction Requirements: MSc or 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 insights into business value Passionate More ❯

What will you be doing? Building reports and dashboards to track KPIs. Creating data products (dbt models) for the organization. Developing classification and regression ML models using techniques like random forests and gradient boosting. Identifying data gaps and working with Software Engineers to address them. Analyzing A/B test results and hypothesis testing. Contributing to the development of More ❯

Y being predicted and independent variable X1, X2, X3..........Xn . Here x is a predictor and Y is function of X variables. The simple equation of linear regression is- Random forest regression is a bagging technique where the parts of the main dataset get distributed among multiple Decision Trees that will predict the best model. And finally based … on the root mean square error(RMSE), it will aggregate the best model or choose the best predictive model. In Random Forest Process, we have some base learner models like M1, M2, M3 .. Mn . These base learner model are called Decision Trees . Each decision tree will randomly pickup the number of rows and columns from … model. When you create an experiment, Automated ML will create multiple models for you. Based on the normalized root mean squared error , we will select our best model i.e. Random Forest and deploy as a web service Here you need to provide some details like name of the model and compute type Click on Deploy Note : If we More ❯

ongoing don't miss your chance to secure the future of your career! Contact me @ j.shaw- bollands@tenthrevolution.com or on 0191 338 6641. Data Science, Data Scientist, AI, ML, Random Forest, Databricks, SageMaker, Regression, Gradient Boosting, NLP, Palantir, Insurance, Banking #J-18808-Ljbffr More ❯

monitoring, and scaling of models. Qualifications Proven experience as a Machine Learning Engineer, with leadership experience in deploying models in production. Experience with classical ML algorithms (e.g., Logistic Regression, Random Forest, XGBoost), NLP, Transfer Learning, Deep Learning (e.g., BERT, Llama, LLMs). Expertise in end-to-end ML development and applications involving LLMs and frameworks like Langchain. Familiarity More ❯

skills using tools like Tableau, Spotfire, Power BI etc. Strong SQL skills required Experience in Python with predictive modelling, regression techniques as well as wider techniques like clustering/random forest is desirable Tech Stack: SQL, Python, R, Tableau, AWS Athena + More! More information: Enjoy fantastic perks like private healthcare & dental insurance, a generous work from abroad More ❯

packages Experience applying various methods of numerical and categorical modelling techniques and supervised and unsupervised machine learning methods (OLS regression and GLMs, logistic regression, KNN, SVM, decision trees/random forest, clustering and cluster analysis, dimensionality reduction, neural networks) Hands-on development experience working with version control systems (we use Git) Practical experience designing and applying data science More ❯

/or statistical packages e.g. actuarial pricing software Experience in SOME of the following predictive modelling techniques e.g.Logistic Regression, Log-Gamma GLMs, GBMs, Elastic Net GLMs, GAMs, Decision Trees, Random Forests, Support Vector Machines and Neural Nets Experienced in the use of a programming language (e.g. R, Matlab, Python or Octave) Experience of using Emblem and Radar Highly numerate More ❯

subject. A demonstrable passion for machine learning/data science or statistics is crucial. A comprehensive understanding of most of the following would be expected: Generalised Linear Models, CART, Random Forests, Clustering, Principal Components Analysis and Network Theory. Experience of Neural Networks would be a plus, but not essential. Knowledge of Python would be essential as well as good More ❯

members Key Skills and Experience: 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, Statistics, Engineering, Physics, Computer Science More ❯

Manage the full model development lifecycle, including data preparation, exploratory analysis, model training, validation, and deployment. Develop and fine-tune predictive algorithms such as: Classification: Logistic Regression, Decision Trees, Random Forests. Regression: Linear Models, Gradient Boosting, Neural Networks, K-Nearest Neighbors. Build models for customer behavior prediction and risk assessment in insurance, particularly in underwriting and claims. Apply natural More ❯

Manage the full model development lifecycle, including data preparation, exploratory analysis, model training, validation, and deployment. Develop and fine-tune predictive algorithms such as: Classification: Logistic Regression, Decision Trees, Random Forests. Regression: Linear Models, Gradient Boosting, Neural Networks, K-Nearest Neighbors. Build models for customer behavior prediction and risk assessment in insurance, particularly in underwriting and claims. Apply natural More ❯

the risk models and working closely with underwriting and technical modelling teams. ✔️ Personal Lines Pricing experience (Motor, Home or Pet) ✔️ Strong skills in predictive modelling – e.g., GLMs, GBMs, GAMs, Random Forests ✔️ Proficiency in R, Python, PySpark, SAS or SQL ✔️ Experience with Radar and/or Emblem ✔️ A numerate degree (e.g., Maths, Stats, Actuarial, Engineering) ✔️ Strong communication and stakeholder management More ❯

of the ML platform. In this role, you'll draw 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, choosing the right tool for the problem. Your work will also focus on enhancing research workflows to More ❯

of mathematical competence. The ability to code or have programming experience, especially in Python. Some experience with theoretical concepts of statistical learning (e.g. hypothesis testing, Bayesian Inference, Regression, SVM, Random Forests, Neural Networks, Natural Language Processing, optimisation). Experience with some coding libraries frequently used in data science. The ability to communicate effectively. Experience composing and following a project More ❯

Gen AI and machine learning. Experience analyzing large data sets, data cleaning, and statistical analysis. Proven experience with at least three machine learning algorithms (e.g., neural networks, logistic regression, random forests). Proficiency with Java and Python, understanding of data structures, algorithms, and software design patterns. Experience with AI/Gen AI frameworks like TensorFlow or PyTorch. Experience with More ❯

What you'll bring Solid 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 area (e.g. NLP, Transfer Learning etc) and modern Deep Learning algorithms (e.g. BERT, LSTM, etc) Solid knowledge of SQL and More ❯

Python, Tensorflow (essential) Database experience, preferably SQL (essential) Expertise in cutting-edge AI methodologies, including Generative AI and Reinforcement Learning Machine learning - Supervised/unsupervised learning, regression, decision trees, random forests, boosting, clustering (essential) The rewards There's one thing people can't stop talking about when it comes to #LifeAtSky : the perks. Here's a taster: Sky Q More ❯