8 of 8 Remote Random Forest Jobs

Learning based products Proven experience 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 (Jupyter, Pandas, Scikit Learn, Matplotlib 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 ❯

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 ❯

Skills and Experience: Previous experience within Personal Lines Pricing is advantageous 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 statistical and data science programming languages (e.g. R, Python, PySpark, SAS, SQL) A good quantitative degree (Mathematics, Statistics, Engineering, Physics, Computer 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 ❯

or statistical packages e.g. actuarial pricing software 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 Experienced in the use of a programming language (e.g. R, Matlab, Python or Octave) Experience of using Emblem and Radar Highly numerate More ❯