7 of 7 Remote Random Forest Jobs

Mining, Data Quality, Data Visualization, Deep Learning, Graph Algorithms, High-Performance Computing, Logistic Regression Model, Machine Learning (Ml), Master Data Management, Pandas (Python), Python (Programming Language), R (Programming Language), Random Forest Algorithm, Sql (Structured Query Language), Statistical Modeling, Time Series Analysis More ❯

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-driven solutions. More ❯

for clients. Create segmentation models using multinomial logistic regression and linear discriminant analysis. Advanced Analytics Skills Strong working knowledge of analytical techniques such as conjoint analysis, machine learning (e.g., Random Forests, SVM), statistical methods (e.g., regression), time series, basket analysis, and unstructured data analytics. Ability to synthesize multiple data sources into meaningful insights and actionable business metrics. Knowledge of More ❯

systems, complexity economics, or ergodic economics. • Familiarity working in cloud-based computing environments like Amazon Web Services, Google Cloud, or Microsoft Azure. • Experience applying various machine learning approaches (e.g., random forest, neural networks, support vector machines). • Experience working with databases (e.g., PostgreSQL, Oracle, MySQL, MongoDB, Neo4J). • Experience using version control (e.g., Git, Mercurial, SVN) to support More ❯

knowledge of current trends and issues in motor or home pricing Experience with some of the following predictive modelling techniques; Logistic Regression, GBMs, Elastic Net GLMs, GAMs, Decision Trees, Random Forests, Neural Nets and Clustering. Knowledge of the technical differences between different packages for some of these model types would be an advantage. Experience in statistical and data science More ❯

Previous experience within data science Experience in commercial pricing and modelling, ideally with a focus in Motor Experience and detailed technical knowledge of GLMs/Elastic Nets, GBMs, GAMs, Random Forests, and clustering techniques Experience in programming languages (e.g. Python, PySpark, R, SAS, SQL) Proficient at communicating results in a concise manner both verbally and written Behaviours: Motivated by More ❯

Previous experience within data science Experience in commercial pricing and modelling, ideally with a focus in Motor Experience and detailed technical knowledge of GLMs/Elastic Nets, GBMs, GAMs, Random Forests, and clustering techniques Experience in programming languages (e.g. Python, PySpark, R, SAS, SQL) Proficient at communicating results in a concise manner both verbally and written Behaviours: Motivated by More ❯