21 of 21 Logistic Regression Jobs in England

science projects and manage deliverables Qualifications Solid understanding of computer science fundamentals, including data structures, algorithms, data modelling, and software architecture Solid understanding of classical Machine Learning algorithms (e.g., Logistic Regression, Random Forest, XGBoost, etc.), state-of-the-art research areas (e.g., NLP, Transfer Learning, etc.), and modern Deep Learning algorithms (e.g., BERT, LSTM, etc.) Solid knowledge of More ❯

Data Science, AI, or a related discipline. Strong numerical and statistical knowledge (e.g. Bayesian/frequentist methods, probability distributions). Solid understanding of machine learning algorithms (e.g. linear/logistic regression, random forests, neural networks, time series models). Experience with multiple programming languages — with Python and SQL preferred. Interest or familiarity with large language models and prompt More ❯

AI/Gen AI and machine learning. Experience analyzing large data sets, data cleaning, and statistical analysis. Proven experience with at least three machine learning algorithms (e.g., neural networks, logistic regression, random forests). Proficiency with Java and Python, understanding of data structures, algorithms, and software design patterns. Experience with AI/Gen AI frameworks like TensorFlow or More ❯

Salary Range $93,800.00 - $174,200.00 Skills Desired Biostatistics, Computer Programming, Data Analysis, Databases, Data Management, Data 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 ❯

for deployment, monitoring, and scaling of models. Qualifications Proven experience as a Machine Learning Engineer, with leadership experience in deploying models in production. Experience with classical ML algorithms (e.g., Logistic Regression, Random Forest, XGBoost), NLP, Transfer Learning, Deep Learning (e.g., BERT, Llama, LLMs). Expertise in end-to-end ML development and applications involving LLMs and frameworks like More ❯

decisions and strategic changes to prices to meet budget requirements. 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 ❯

vision and long-term goals of the company. Key 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 More ❯

vision and long-term goals of the company. Key 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 More ❯

vision and long-term goals of the company. Key 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 More ❯

vision and long-term goals of the company. Key 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 More ❯

Mathematics, Statistics, Data Science, Economics, or Physics 1 to 2 years of experience in a Financial data driven environment, or strong academic project experience Familiarity with modelling techniques like logistic regression or basic machine learning A keen interest in data science and its applications in finance or risk Strong attention to detail and a problem solving mindset A More ❯

Mathematics, Statistics, Data Science, Economics, or Physics 1 to 2 years of experience in a Financial data driven environment, or strong academic project experience Familiarity with modelling techniques like logistic regression or basic machine learning A keen interest in data science and its applications in finance or risk Strong attention to detail and a problem solving mindset A More ❯

Gen AI within their organisation and are looking to utilise the newest technologies on the market. Key Responsibilities: Design and implement machine learning models for both supervised (e.g., classification, regression) and unsupervised learning tasks. Manage the full model development lifecycle, including data preparation, exploratory analysis, model training, validation, and deployment. Develop and fine-tune predictive algorithms such as: Classification … Logistic Regression, Decision Trees, Random Forests. Regression: Linear Models, Gradient Boosting, Neural Networks, K-Nearest Neighbors. Build models for customer behavior prediction and risk assessment in insurance, particularly in underwriting and claims. Apply natural language processing (NLP) for tasks like document classification and information extraction using OCR. Utilize cloud-based platforms like AWS SageMaker and Databricks for More ❯

Gen AI within their organisation and are looking to utilise the newest technologies on the market. Key Responsibilities: Design and implement machine learning models for both supervised (e.g., classification, regression) and unsupervised learning tasks. Manage the full model development lifecycle, including data preparation, exploratory analysis, model training, validation, and deployment. Develop and fine-tune predictive algorithms such as: Classification … Logistic Regression, Decision Trees, Random Forests. Regression: Linear Models, Gradient Boosting, Neural Networks, K-Nearest Neighbors. Build models for customer behavior prediction and risk assessment in insurance, particularly in underwriting and claims. Apply natural language processing (NLP) for tasks like document classification and information extraction using OCR. Utilize cloud-based platforms like AWS SageMaker and Databricks for More ❯

Gen AI within their organisation and are looking to utilise the newest technologies on the market. Key Responsibilities: Design and implement machine learning models for both supervised (e.g., classification, regression) and unsupervised learning tasks. Manage the full model development lifecycle, including data preparation, exploratory analysis, model training, validation, and deployment. Develop and fine-tune predictive algorithms such as: Classification … Logistic Regression, Decision Trees, Random Forests. Regression: Linear Models, Gradient Boosting, Neural Networks, K-Nearest Neighbors. Build models for customer behavior prediction and risk assessment in insurance, particularly in underwriting and claims. Apply natural language processing (NLP) for tasks like document classification and information extraction using OCR. Utilize cloud-based platforms like AWS SageMaker and Databricks for 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 ❯

Management frameworks and associated regulations (e.g. SS1/23, Consumer Duty) Familiarity with data science processes (e.g. pipelines) and associated technologies Exposure to range of risk modelling techniques (e.g. logistic and time series regressions and Machine Learning methods, such as gradience boosting, random forests, etc.) Red Hot Rewards Generous holidays - 38.5 days annual leave (including bank holidays and prorated More ❯

long-term strategy and growth objectives Key Skills and Experience Substantial experience within Personal Lines Pricing, ideally including team or project leadership Proficiency in predictive modelling techniques such as Logistic Regression, GBMs, Elastic Net GLMs, GAMs, Decision Trees, Random Forests, Neural Nets, and Clustering Strong skills in R, Python, PySpark, SAS, or SQL Proven ability to interpret performance More ❯

long-term strategy and growth objectives Key Skills and Experience Substantial experience within Personal Lines Pricing, ideally including team or project leadership Proficiency in predictive modelling techniques such as Logistic Regression, GBMs, Elastic Net GLMs, GAMs, Decision Trees, Random Forests, Neural Nets, and Clustering Strong skills in R, Python, PySpark, SAS, or SQL Proven ability to interpret performance More ❯

Linux, Airflow. An appreciation of the main concepts of Data Science/Machine Learning would be useful. We use a range of predictive analytics and machine learning methodologies, including logistic regression and cluster analysis, plus some predictive time series analysis. We are committed to creating a diverse environment and are proud to be an equal-opportunity employer. All More ❯

analysis Experienced in the use of programming language (e.g. SAS) and/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 More ❯