13 of 13 Permanent Logistic Regression Jobs

application of Data Science principals and experience with data science methodologies. Experience with the following statistical and predictive modeling approaches: Gaussian Process; Markov Models; Hierarchical Clustering; K-Means; Linear Regression; Logistic Regression; Monte Carlo Simulation; Neural Networks. Extensive technical skills, consulting experience, and business savvy to interface with all levels and disciplines within our customers' organization. Demonstrated More ❯

application of Data Science principals and experience with data science methodologies. Experience with the following statistical and predictive modeling approaches: Gaussian Process; Markov Models; Hierarchical Clustering; K-Means; Linear Regression; Logistic Regression; Monte Carlo Simulation; Neural Networks. Extensive technical skills, consulting experience, and business savvy to interface with all levels and disciplines within our customers' organization. Demonstrated More ❯

projects and manage deliverables What we offer Solid understanding of computer science fundamentals, 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 areas (e.g. NLP, Transfer Learning) and modern Deep Learning algorithms (e.g. BERT, LSTM) Solid knowledge of SQL and Python More ❯

projects, including survey, database, and hybrid segmentations using multiple data sources. Apply best practices in segmentation to produce high-quality, actionable insights for clients. Create segmentation models using multinomial logistic regression and linear discriminant analysis. Advanced Analytics Skills Strong working knowledge of analytical techniques such as conjoint analysis, machine learning (e.g., Random Forests, SVM), statistical methods (e.g., regression More ❯

Solid project management skills Demonstrated experience building and maintaining client relationships Commercially focused mindset Coaching, Leadership and Management experience Technical skillset: Segmentation & Discriminant analysis & tools (essential) Predictive modelling (e.g. logistic regression) (essential) Choice/Allocation based models (essential) Decision/Regression based trees (essential) Key Driver analysis methods (essential) Multivariate analysis (essential) Machine learning/AI methods More ❯

build models and generate insights. Design and execute experiments (e.g., A/B tests, causal impact studies) to evaluate business strategies. Build and interpret models such as linear/logistic regression and clustering, and understand their assumptions and applications. Create clear, inclusive visualizations and dashboards that communicate insights to technical and non-technical audiences. Collaborate with stakeholders to 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 ❯

machine learning engineers and share best practices Key Skills and Experience: Previous experience in tuning and deploying machine learning methods 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 DevOps and Azure ML, or other MLOps and ML Lifecycle technology stacks More ❯

machine learning engineers and share best practices Key Skills and Experience: Previous experience in tuning and deploying machine learning methods 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 DevOps and Azure ML, or other MLOps and ML Lifecycle technology stacks 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 ❯

scalable, fault-tolerant, distributed systems utilising AWS services such as API Gateway, Lambda, DynamoDB, SQS, SNS, and EventBridge. Are knowledgeable about AI and machine learning fundamentals, including decision trees, logistic regression, and neural networks. Have prior experience integrating OpenAI or constructing Agentic AI systems, and are open to utilising AI tools like Cursor. Demonstrate core software engineering competencies 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 ❯