26 to 50 of 54 Logistic Regression Jobs

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 ❯

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 ❯

effective exploratory data analysis, and model building using industry standard time Series Forecasting techniques like ARIMA, ARIMAX, Holt Winter and formulate ensemble model. Proficiency in both Supervised (Linear/Logistic Regression) and Unsupervised algorithms (k means clustering, Principal Component Analysis, Market Basket analysis). Experience in solving optimization problems like inventory and network optimization. Should have hands on More ❯

Proficiency: Strong skills with libraries like pandas, NumPy, scikit-learn, PyTorch, and Hugging Face Transformers. Ability to write 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 More ❯

Python Proficiency: Strong coding skills using libraries like 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 More ❯

Python Proficiency: Strong coding skills using libraries like 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 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 ❯

e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience. 5+ years of data scientist experience. Experience with statistical models e.g. multinomial logistic regression. PREFERRED QUALIFICATIONS Experience working with data engineers and business intelligence engineers collaboratively. Experience managing data pipelines. Experience as a leader and mentor on a data science team. Amazon 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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

of experimentation methodologies (A/B testing, multivariate testing, etc.) and their application in product decision-making. In-depth knowledge of statistical and machine learning models: gradient boosted trees, logistic regression, neural networks, survival analysis, etc. Experience with end-to-end model development and maintenance of ML models used for business-critical decisions. Solid understanding of key product … you built or launched AI or ML-powered products in a professional setting? * Select... Which statistical or machine learning models have you worked with extensively? (Select all that apply) * Logistic Regression Survival Analysis Other Have you worked in or supported a product related to search or discovery (e.g., eCommerce search engines, recommender systems)? * Select... #J-18808-Ljbffr 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 ❯

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 ❯

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 ❯

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 ❯