1 to 25 of 77 Random Forest Jobs in the UK

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 ❯

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 ❯

Develop a thorough understanding of the data science lifecycle, including data exploration, preprocessing, modelling, validation, and deployment. Design, build, and maintain tree-based predictive models, such as decision trees, random forests, and gradient-boosted trees, with a low-level understanding of their algorithms and functioning. Hyperparameter tuning for existing machine learning models to optimize performance. Collaborate with cross-functional More ❯

Develop a thorough understanding of the data science lifecycle, including data exploration, preprocessing, modelling, validation, and deployment. Design, build, and maintain tree-based predictive models, such as decision trees, random forests, and gradient-boosted trees, with a low-level understanding of their algorithms and functioning. Hyperparameter tuning for existing machine learning models to optimize performance. Collaborate with cross-functional More ❯

working to support the senior data modeller in creating, developing, and iteratively improving models based on historic trends. The models will vary in design, but current models include XGboost, Random Forest and MCMC models. This role will also create analysis and provide support to the pricing team, to increase their automation and accuracy of analysis in the financial … that benefit both customers and clients. The Person Experience of using machine learning techniques on real data is needed for this role. Desirable if the candidate has experience of random forests, XGboosts or Monte Carlo. Proficiency in Python for data manipulation and model development. Otherwise, proficiency in another language (such as R, Java etc) with the ability to learn More ❯

Experience: 10+ years Job Description: Must have good experience in implementing machine learning models such as Prophet, ARIMA, SARIMA, XGBoost, ElasticNet, Ridge, Lasso, Random Forest, and Linear Regression on time-series data. Proficient in using Python ML packages such as scikit-learn, sktime, and darts. Must have strong expertise in key techniques for time series feature engineering, including More ❯

Experience: 10+ years Job Description: Must have good experience in implementing machine learning models such as Prophet, ARIMA, SARIMA, XGBoost, ElasticNet, Ridge, Lasso, Random Forest, and Linear Regression on time-series data. Proficient in using Python ML packages such as scikit-learn, sktime, and darts. Must have strong expertise in key techniques for time series feature engineering, including More ❯

clinical trial exposure. Strong knowledge of statistical methods like time-to-event analysis, machine learning, meta-analysis, mixed-effect modeling, Bayesian methods, variable selection techniques (e.g., lasso, elastic net, random forest), and clinical trial design. Proficiency in R and Python, with experience in data visualization, exploratory analysis, and predictive modeling. Excellent communication skills, both verbal and written. Ability 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 ❯

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 ❯

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 ❯

knowledge of current trends and issues in motor or home 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. Knowledge of the technical differences between different packages for some of these model types would be an advantage. Experience in statistical and data science More ❯

knowledge of current trends and issues in motor or home 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. Knowledge of the technical differences between different packages for some of these model types would be an advantage. Experience in statistical and data science 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 ❯

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 ❯

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 ❯

Squares (PLS)) to identify key measurement variables within complex electrochemical datasets. Develop non-linear regression models to improve the accuracy of immunoassay data analysis. Apply machine learning techniques, including Random Forest and neural networks, to classify sample types based on electrochemical measurements, supporting biomarker discovery Design and optimise predictive models to identify novel biomarker panels, combining healthcare data … in Python, R, MATLAB, or other statistical programming languages. Knowledge of multivariate analysis techniques (e.g., PCA, PLS) and non-linear regression models. Experience developing predictive machine learning algorithms (e.g., Random Forest, Neural Networks). Proficiency in SQL (preferably MySQL) and database management for engineering data storage Experience working with biomedical data science, bioinformatics or diagnostics is desired but More ❯

Skills and Experience: Previous experience within Personal Lines Pricing is advantageous 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 ❯

Skills and Experience: Previous experience within Personal Lines Pricing is advantageous 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 ❯

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 ❯

scikit-learn, PyTorch, and Hugging Face Transformers. Ability to write 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 ❯

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 ❯