9 of 9 Random Forest Jobs in the UK

times. 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 automation ...

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 ...

such as GraphRAG and agentic RAG to reduce hallucination and improve factual grounding. Technical Proficiency Machine learning algorithms: linear regression, logistic regression, decision trees, random forests, support vector machines, neural networks Data science tools: NumPy, SciPy, Pandas, Matplotlib, TensorFlow, Keras Cloud computing platforms: AWS, Azure, GCP Natural language processing ...

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 ...

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 ...

Expertise: Solid understanding of computer science fundamentals and time-series forecasting. Machine Learning: Strong grasp of ML and deep learning algorithms (e.g. Logistic Regression, Random Forest, XGBoost, BERT, LSTM, NLP, Transfer Learning). Reasonable Adjustments: Respect and equality are core values to us. We are proud ...

code tool. Docker, CI/CD and Git-based development. API monitoring, logging and production support. Working with models such as neural networks and random forests. Financial services or insurance experience would be useful, but it isn't essential. Strong ML and software engineering fundamentals matter more. Why consider ...

code tool. Docker, CI/CD and Git-based development. API monitoring, logging and production support. Working with models such as neural networks and random forests. Financial services or insurance experience would be useful, but it isn't essential. Strong ML and software engineering fundamentals matter more. Why consider ...