1 to 7 of 7 Random Forest Jobs

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 »

parameter estimation, factors selection, PCA, hypothesis testing, time series, queuing theory, survival analysis, clustering, linear programming. Experience with machine learning methods, such as regularization, random forests, neural networks and deep learning. Ability to write algorithms and implement pipelines in Python. Knowledge of Scala, R, is a plus. Experienced in more »

Solid understanding of computer science fundamentals, including data structures, algorithms, data modeling, and software architecture. Proficiency in classical machine learning algorithms (e.g., Logistic Regression, Random Forest, XGBoost) and modern deep learning algorithms (e.g., BERT, LSTM). Strong knowledge of SQL and Python's data analysis ecosystem (Jupyter, Pandas 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 »

Solid understanding of computer science fundamentals, including data structures, algorithms, data modeling, and software architecture. Proficiency in classical machine learning algorithms (e.g., Logistic Regression, Random Forest, XGBoost) and modern deep learning ... 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 »