1 to 7 of 7 Random Forest Jobs in England

SQL across structured, semi-structured and unstructured data. Ability to use dimension reduction techniques (PCA, encoders etc.) Excellent familiarity with elastic net logistic regression, random forest and XGBoost ensembles to work on supervised problems with structured, tabular data. We currently use Scikit-learn, and we're open to more »

ML model building using R and/or Python languages (generalized linear models, logistics/linear Regression, Tree based and Boosting models, Decision trees, Random Forest, XGBoost etc.- Basic understanding of Neural network and deep learning models). Possess experience with Machine learning/Pattern recognition areas. Should more »

Possess vast experience and expertise of working with probability and statistics, inclusive of machine learning, experimental design, and optimisation Experience using Gradient Boosting Machines, Random Forest, Neural Network or similar algorithms Proven and successful track record of leading high-performing data analyst teams through the successful performance of more »

Possess vast experience and expertise of working with probability and statistics, inclusive of machine learning, experimental design, and optimisation Experience using Gradient Boosting Machines, Random Forest, Neural Network or similar algorithms Proven and successful track record of leading high-performing data analyst teams through the successful performance of more »

Machine Learning exposurePassion for algorithmic trading (previous experience not required)Does this sound interesting?Data Scientist Key Words: VBA, Excel, Financial, Finance, Data, Analytics, Random Forests, Decision Trees, Python, R, SQL, Mathematics, Statistics, Statistical, Predictions, Predictive, AI, Machine Learning, Deep Learning, Modelling, Algorithms, Pandas, NumPy, PyTorch, TensorFlowJob typePermanent more »

related analytical background. Proficiency in programming languages such as SAS, R, Matlab, or Python. Experience with predictive modelling techniques like Logistic Regression, Decision Trees, Random Forests, etc. Familiarity with Emblem and Radar. Strong communication skills to convey statistical concepts to non-statistical audiences. Self-motivated with excellent planning and more »

e.g. SAS Experience in some of the following predictive modelling techniques e.g. Logistic regression, Log-Gamma GLMs, GBMs, Elastic Net GLMs, GAMs, Decision Trees, Random Forests, Support Vector Machines and Neural Nets. Experience of Emblem and Radar. Experience in the use of programming language such as Matlab and Python. more »