51 to 64 of 64 Decision Tree Jobs in England

like 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 More ❯

like 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 More ❯

products and features that often require novel innovations. We are investing in AI to build better search, discovery, and workflow solutions using technologies such as transformers, gradient boosted decision trees, large language models, and dense vector databases. We are expanding our group and seeking highly skilled individuals who will be responsible for contributing to the team (or teams More ❯

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. … 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 the main dataset, the process is called Row Sampling for rows distribution and Feature Sampling for columns distribution. In … this way every base learner/decision tree will have D’ dataset. This will form a bootstrap model which will be aggregated according to the bagging process. This information is well enough to understand working process of our model. The workspace is the top level of resource which you need to build to work in Machine Learning More ❯

like 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 More ❯

Engineering and CTO Ref # 10043101 Description & Requirements Bloomberg's Engineering AI department has 300+ AI practitioners building innovative products that leverage technologies such as transformers, gradient boosted decision trees, large language models, and dense vector databases. We are expanding our team and seeking skilled individuals to contribute to AI-driven customer-facing products. Our goal is to More ❯

bots across various platforms (web, mobile, messaging apps and telephony). Ensure seamless integration with existing systems. Coding Proficiency: Write and maintain code snippets for chatbot logic, including decision trees, intents, and responses. User Experience (UX): Collaborate with UX designers to create intuitive and user-friendly chatbot interfaces. Documentation: Maintain process flows to reflect the flows within the More ❯

Knowledge/Experience: Proven success in Cards and/or Fraud Management disciplines Must have experience with tools such as Python/SQL, Tableau/PowerBI, decision tree software ( Angoss) Prior experience and knowledge of Cards fraud is essential Working knowledge of card authorization and an understanding of how to reduce authorization declines is required Understanding of … or equivalent experience in a related field Skills: Exceptional analytical skills Superior verbal and written skills Proficient in tools such as MS Office, Excel and Access, decision tree software Working knowledge of SQL and databases required Job Family Group: Operations - Services Job description: This role will be responsible for developing and managing fraud prevention and detection strategies … Knowledge/Experience: Proven success in Cards and/or Fraud Management disciplines Must have experience with tools such as Python/SQL, Tableau/PowerBI, decision tree software ( Angoss) Prior experience and knowledge of Cards fraud is essential Working knowledge of card authorization and an understanding of how to reduce authorization declines is required Understanding of More ❯

the following experience: Proven experience in General Insurance Pricing (Personal Lines preferred) Strong coding skills in Python, R, SQL, PySpark and SAS Experience with modelling techniques (GLMs, GBMs, Decision Trees, Neural Nets, Clustering) Exposure to or expertise in WTW's Radar and Emblem software Excellent communication skills — both written and verbal — with a commercial mindset Leadership candidates will More ❯

for unsecured lending products, ideally with credit cards; familiarity working with third-party fraud detection tools.* **Technical Skills**: Proficiency in SQL or Python for data analysis; familiarity with decision trees or other predictive modelling is a plus* **Domain Knowledge**: Familiarity with applicable laws and regulations that impact Zendable’s business including BSA, OFAC, GLBA, TILA including the Credit More ❯

ability to explain technical concepts to non-technical audiences and influence VP-level stakeholders. Strong understanding of logic-based workflows, with the ability to confidently design and troubleshoot decision trees, conditional logic, and automation flows. Comfort operating in ambiguity, solving complex problems, and staying on track amidst shifting priorities. A curious and resilient mindset, with a bias for More ❯

to tabulate and analyze quantitative consumer research data effectively. This also includes running advanced statistical analysis which includes, but is not limited to, correspondence analysis, CHAID (decision tree analysis), cluster analysis (segmentation), and forecasting. Maintaining internal databases: Gathering market data through desk research and managing an internal database of the macro-economic environment. Who we are looking More ❯

of others: Contribute, author, or maintain (as appropriate) and communicate a set of architecture artefacts: Standards & policies Guard rails Patterns and anti-patterns Guidance on choice (decision tree, cloud assessment, etc.) Principles Defining and maintain a set of architecture building blocks jointly with security architects Key solution building blocks, jointly with solution architects and/or security More ❯

of others: Contribute, author, or maintain (as appropriate) and communicate a set of architecture artefacts: Standards & policies Guard rails Patterns and anti-patterns Guidance on choice (decision tree, cloud assessment, etc.) Principles Defining and maintain a set of architecture building blocks jointly with security architects Key solution building blocks, jointly with solution architects and/or security More ❯