51 to 61 of 61 Logistic Regression Jobs

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 ❯

as is a general willingness to constantly learn and improve one's technical skill set. This role also requires the ability to conduct statistical modeling (such as linear and logistic regression) in order to inform divisional strategy, and work with non-technical stakeholders to understand key questions and problems and use a variety of approaches to address them 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 ❯

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 ❯

and ambition, yet remains kind and caring. Experience Needed A CRM or marketing related analytics background is essential – so you understand things like segmentations and how to do them. Logistic regression and predictive analytics, good knowledge of data science techniques within a marketing/commercial environment (not product related) will be beneficial Solid analytics skills, with the story 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 ❯

processes, and partner with internal and external stakeholders to deliver data-driven solutions. Python. Git for version control. Classical statistics. Hypothesis testing (parametric and non-parametric). Linear or logistic regression models. Exposure to small datasets, not just large (very important). Familiarity with pharma/health/medical datasets. Mixed effects models – ability to use matching techniques More ❯

SQL (Google BigQuery) and Python. Strong foundational knowledge with practical experience in designing and analyzing A/B tests. Well-versed in various analysis techniques, such as linear and logistic regression, significance testing, and statistical modeling. Previous experience in the IAM domain is a strong plus but not a must. This is an excellent opportunity to work with More ❯

and venues to support operational realistic environments and data requirements. Utilize advanced knowledge of mathematics and statistical and engineering methods in determining solutions to complex problems including multivariate and logistic regression analysis, hypothesis testing, design of experiments, parametric and nonparametric procedures, and distributional analysis. Required Experience: Active DOD TOP SECRET clearance with SCI eligibility Bachelors degree in science More ❯