1 to 25 of 28 Permanent Logistic Regression Jobs

and structure win-win commercial deals. Strong hands-on skills in Excel and PowerPoint, including financial modeling and executive presentations. Solid understanding of statistical modeling techniques such as Linear Regression, Logistic Regression, and Bayesian methods. Excellent communication skills-both written and verbal-with the ability to clearly present complex concepts to non-technical stakeholders. Leadership presence with More ❯

assess and articulate regulatory implications and expectations of distinct modeling efforts. Advanced experience with the concepts and technologies associated with classical supervised modeling for prediction such as linear/logistic regression, discriminant analysis, support vector machines, decision trees, forest models, etc. Advanced experience with the concepts and technologies associated with unsupervised modeling such as k-means clustering, hierarchical More ❯

assess and articulate regulatory implications and expectations of distinct modeling efforts. Advanced experience with the concepts and technologies associated with classical supervised modeling for prediction such as linear/logistic regression, discriminant analysis, support vector machines, decision trees, forest models, etc. Advanced experience with the concepts and technologies associated with unsupervised modeling such as k-means clustering, hierarchical More ❯

assess and articulate regulatory implications and expectations of distinct modeling efforts. Advanced experience with the concepts and technologies associated with classical supervised modeling for prediction such as linear/logistic regression, discriminant analysis, support vector machines, decision trees, forest models, etc. Advanced experience with the concepts and technologies associated with unsupervised modeling such as k-means clustering, hierarchical More ❯

assess and articulate regulatory implications and expectations of distinct modeling efforts. Advanced experience with the concepts and technologies associated with classical supervised modeling for prediction such as linear/logistic regression, discriminant analysis, support vector machines, decision trees, forest models, etc. Advanced experience with the concepts and technologies associated with unsupervised modeling such as k-means clustering, hierarchical More ❯

linking, named entity matching, deduplication/disambiguation). Experience performing data mining, analysis, and training set construction. Experience with supervised machine learning methods including Decision Trees, Support Vector Machines, Logistic Regression, Random/Rotation Forests, Categorization/Classification, Neural Nets, Bayesian Networks, etc. Experience with unsupervised machine learning methods including Cluster Analysis (e.g., K-means, K-nearest Neighbor More ❯

assess and articulate regulatory implications and expectations of distinct modeling efforts. Advanced experience with the concepts and technologies associated with classical supervised modeling for prediction such as linear/logistic regression, discriminant analysis, support vector machines, decision trees, forest models, etc. Advanced experience with the concepts and technologies associated with unsupervised modeling such as k-means clustering, hierarchical More ❯

a related role. Prior work experience must include three (3) years of experience with the following: statistics concepts, including descriptive statistics, data distribution models, Time Series Analysis, correlation, and regression, and its application to data; programming languages, such as Python, Java, or C++; visualization tools, such as Tableau or Qlik; text analytics and NLP using python; machine learning algorithms … and exposure to supervised and unsupervised learning techniques including Linear/Logistic Regression, SVM, Random Forest and Boosting, Clustering, and Patterns Recognition; utilizing SQL to perform ad-hoc analysis for audit testing, such as determining a population or re-performance; and engineering methodologies (Git, CI/CD, Agile) and SDLC best practices. Job Code: . Salary Range: Annual More ❯

scientific principles, tools, and analytical techniques, to be reflected throughout most of the relevant prior experience. • Demonstrated experience and proficiency with data science techniques including (but not limited to) logistic regression, vector, natural language processing, principal component analysis, clustering techniques, neural networks, deep learning models, image recognition and algorithm employment or development in support of any of the More ❯

scientific principles, tools, and analytical techniques, to be reflected throughout most of the relevant prior experience. • Demonstrated experience and proficiency with data science techniques including (but not limited to) logistic regression, vector, natural language processing, principal component analysis, clustering techniques, neural networks, deep learning models, image recognition and algorithm employment or development in support of any of the More ❯

impact of pricing proposals Coaching and mentoring junior team members Key Skills and Experience: Previous experience within general insurance 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 Experience in statistical and data science programming languages (e.g. R, Python, PySpark, SAS, SQL More ❯

impact of pricing proposals Coaching and mentoring junior team members Key Skills and Experience: Previous experience within general insurance 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 Experience in statistical and data science programming languages (e.g. R, Python, PySpark, SAS, SQL More ❯

general insurance products and/or pricing teams, including 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 More ❯

general insurance products and/or pricing teams, including 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 More ❯

general insurance products and/or pricing teams, including 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 More ❯

general insurance products and/or pricing teams, including 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 More ❯

defining the foundations and best practices to allow automation and smart solutions for our customers. Tools experience that is beneficial: Python (required), SQL, Power BI. Good experience using XGBoost & Logistic regression models as a minimum. More ❯

defining the foundations and best practices to allow automation and smart solutions for our customers. Tools experience that is beneficial: Python (required), SQL, Power BI. Good experience using XGBoost & Logistic regression models as a minimum. More ❯

defining the foundations and best practices to allow automation and smart solutions for our customers. Tools experience that is beneficial: Python (required), SQL, Power BI. Good experience using XGBoost & Logistic regression models as a minimum. More ❯

defining the foundations and best practices to allow automation and smart solutions for our customers. Tools experience that is beneficial: Python (required), SQL, Power BI. Good experience using XGBoost & Logistic regression models as a minimum. More ❯

defining the foundations and best practices to allow automation and smart solutions for our customers. Tools experience that is beneficial: Python (required), SQL, Power BI. Good experience using XGBoost & Logistic regression models as a minimum. More ❯

on coding experience within various traditional (SAS, SQL, etc.) and/or open source (i.e. Python, Impala, Hive, etc.) tools.Traditional and advanced machine learning techniques and algorithms, such as Logistic Regression, Gradient Boosting, Random Forests, etc.Data visualization tools, such as TableauExcellent quantitative and analytic skills; ability to derive patterns, trends and insights, and perform risk/reward trade More ❯

on coding experience within various traditional (SAS, SQL, etc.) and/or open source (i.e. Python, Impala, Hive, etc.) tools.Traditional and advanced machine learning techniques and algorithms, such as Logistic Regression, Gradient Boosting, Random Forests, etc.Data visualization tools, such as TableauExcellent quantitative and analytic skills; ability to derive patterns, trends and insights, and perform risk/reward trade More ❯

developing insightful management information (MI) and presenting data-driven narratives. Effective time management and prioritisation skills. Experience with Python (required), SQL, and Power BI. Practical experience with XGBoost and logistic regression models (minimum). Benefits Private Medical Care: Access to premium healthcare services. Health Cash Plan: Contributions toward everyday medical expenses, including digital physio and skin clinic access. More ❯

developing insightful management information (MI) and presenting data-driven narratives. Effective time management and prioritisation skills. Experience with Python (required), SQL, and Power BI. Practical experience with XGBoost and logistic regression models (minimum). Benefits Private Medical Care: Access to premium healthcare services. Health Cash Plan: Contributions toward everyday medical expenses, including digital physio and skin clinic access. More ❯