1 to 25 of 73 Permanent Random Forest Jobs in England

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 ❯

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 ❯

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 ❯

and manage deliverables Qualifications 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 areas (e.g., NLP, Transfer Learning, etc.), and modern Deep Learning algorithms (e.g., BERT, LSTM, etc.) Solid knowledge of SQL and More ❯

and manage deliverables Qualifications 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 areas (e.g., NLP, Transfer Learning, etc.), and modern Deep Learning algorithms (e.g., BERT, LSTM, etc.) Solid knowledge of SQL and 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 ❯

Manage multiple data science projects and deliverables Qualifications Strong understanding of computer science fundamentals: data structures, algorithms, data modeling, software architecture Experience with classical ML algorithms (e.g., Logistic Regression, Random Forest, XGBoost), research areas (e.g., NLP, Transfer Learning), and Deep Learning (e.g., BERT, LSTM) Proficiency in SQL and Python (Jupyter, Pandas, Scikit-learn, Matplotlib) Knowledge of model evaluation More ❯

Manage multiple data science projects and deliverables Qualifications Strong understanding of computer science fundamentals: data structures, algorithms, data modeling, software architecture Experience with classical ML algorithms (e.g., Logistic Regression, Random Forest, XGBoost), research areas (e.g., NLP, Transfer Learning), and Deep Learning (e.g., BERT, LSTM) Proficiency in SQL and Python (Jupyter, Pandas, Scikit-learn, Matplotlib) Knowledge of model evaluation 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 ❯

linear algebra, and numerical methods Experience with statistical and machine learning models, such as regression-based models (e.g., logistic regression, linear regression, negative binomial regression), tree-based models (e.g., random forests), support vector machines, PCA, clustering models, matrix factorization, deep learning, etc Experience using statistical and machine learning models to contribute to company growth efforts, impacting revenue and other More ❯

statistics, linear algebra, and numerical methodsExperience with statistical and machine learning models, such as regression-based models (e.g., logistic regression, linear regression, negative binomial regression), tree-based models (e.g., random forests), support vector machines, PCA, clustering models, matrix factorization, deep learning, etcExperience using statistical and machine learning models to contribute to company growth efforts, impacting revenue and other key 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 ❯

above). Strong numerical background with a knowledge of key statistical principals e.g. Bayesian and frequentist statistics, probability distributions. Fundamental understanding of ML algorithms e.g. linear and logistic regression, random forest, neural networks, time series models. Experience with multiple programming languages, with a preference for SQL and Python. Familiarity with large language models and prompt engineering would be More ❯

above). Strong numerical background with a knowledge of key statistical principals e.g. Bayesian and frequentist statistics, probability distributions. Fundamental understanding of ML algorithms e.g. linear and logistic regression, random forest, neural networks, time series models. Experience with multiple programming languages, with a preference for SQL and Python. Familiarity with large language models and prompt engineering would be More ❯

above). Strong numerical background with a knowledge of key statistical principals e.g. Bayesian and frequentist statistics, probability distributions. Fundamental understanding of ML algorithms e.g. linear and logistic regression, random forest, neural networks, time series models. Experience with multiple programming languages, with a preference for SQL and Python. Familiarity with large language models and prompt engineering would be More ❯

above). Strong numerical background with a knowledge of key statistical principals e.g. Bayesian and frequentist statistics, probability distributions. Fundamental understanding of ML algorithms e.g. linear and logistic regression, random forest, neural networks, time series models. Experience with multiple programming languages, with a preference for SQL and Python. Familiarity with large language models and prompt engineering would be More ❯

processing of and cleaning of data, merging/joining disparate data sources, feature engineering, performing analyses and communicating results). Produce models using a variety of algorithms (GLM, GBM, Random Forest, Neural Networks etc.) and assess the relative strength of each model Identify which factors are relevant and predictive and should be included in the model build Document More ❯

processing of and cleaning of data, merging/joining disparate data sources, feature engineering, performing analyses and communicating results). Produce models using a variety of algorithms (GLM, GBM, Random Forest, Neural Networks etc.) and assess the relative strength of each model Identify which factors are relevant and predictive and should be included in the model build Document 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 ❯

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 ❯

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 ❯

humility and a desire to learn. A preference to work in a fast-paced and unstructured environment. Nice to haves: Advanced ML skills, covering techniques such as gradient boosting, random forests and neural networks. Prior experience with Snowflake. Prior experience deploying ML models. Salary: Competitive & dependent on seniority, upward from £125,000 + equity Working Policy: Hybrid, with three More ❯

Mining, Data Quality, Data Visualization, Deep Learning, Graph Algorithms, High-Performance Computing, Logistic Regression Model, Machine Learning (Ml), Master Data Management, Pandas (Python), Python (Programming Language), R (Programming Language), Random Forest Algorithm, Sql (Structured Query Language), Statistical Modeling, Time Series Analysis More ❯

AI, or a related discipline. Strong numerical and statistical knowledge (e.g. Bayesian/frequentist methods, probability distributions). Solid understanding of machine learning algorithms (e.g. linear/logistic regression, random forests, neural networks, time series models). Experience with multiple programming languages — with Python and SQL preferred. Interest or familiarity with large language models and prompt engineering (desirable). 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 ❯