9 of 9 Remote/Hybrid Random Forest Jobs in the UK

data science lifecycle across large, multi-source datasets, from analysis and cleansing through to deployment. Apply a broad statistical toolkit, from GLMs and random forest to NLP, clustering and scenario analysis, to generate insight that gets acted on. est and develop new external data sources to keep … modelling and machine learning. Hands-on experience building and evaluating machine learning models. Confident statistical modelling skills across techniques such as GLMs, decision trees, random forest and NLP. Working knowledge of SQL, data structures, ETL and data integration. A degree in Mathematics, Statistics, Data Science or another quantitative ...

concepts and techniques, e.g. skilled in linear algebra.Experience conducting hands-on analytics projects using advanced statistical methods such as generalized regression models, Bayesian methods, random forest, gradient boosting, neural networks, machine learning, clustering, or similar methodologies. Proficiency in programming languages such as Python, Sql etc. Experience with Data ...

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 ...

such as time to event analysis, machine learning, meta-analysis, mixed effect modeling, longitudinal modeling, Bayesian methods, variable selection methods (e.g., lasso, elastic net, random forest), design of clinical trials.Strong programming skills in R and Python. Demonstrated knowledge of data visualization, exploratory analysis, and predictive modeling.Excellent interpersonal ...

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 ...

tuning and deploying machine learning methods 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 DevOps and Azure ML, or other MLOps and ML Lifecycle technology stacks, such as AWS, Databricks, Google ...

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 ...

experience Previous experience within data science Experience in insurance pricing and modelling Experience and detailed technical knowledge of GLMs/Elastic Nets, GBMs, GAMs, Random Forests, Neural Networks and clustering techniques Knowledge of statistics and distributions commonly used in insurance Experience in programming languages (e.g. Python, PySpark ...