19 of 19 Remote Logistic Regression Jobs in the UK

manage deliverables What you'll bring 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 area (e.g. NLP, Transfer Learning etc) and modern Deep Learning algorithms (e.g. BERT, LSTM, etc) Solid knowledge of More ❯

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 ❯

true to our Customers, Owners and Colleagues alike. Essential Experience: Extensive experience designing, developing and deploying machine learning and AI solutions in production environments Statistical modelling, machine learning (e.g. logistic regression, random forest, XGBoost, and modern deep learning techniques (e.g. transformers, transfer learning, reinforcement learning) Proven ability to lead technical direction across projects or domains Expertise in model 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 ❯

querying and programming languages (SQL, R, Python) and visualization tools (Excel, Tableau, matplotlib, seaborn, plotly) Experience with numerical and categorical modeling, supervised and unsupervised machine learning techniques (OLS, GLMs, logistic regression, KNN, SVM, decision trees, clustering, neural networks) Hands-on experience with version control (Git), Big Data, and Cloud platforms (Hadoop, Spark, AWS, GCP) is a strong asset 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 ❯

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 ❯

and Experience Experience managing general insurance products and/or pricing teams. Knowledge of current motor or home pricing trends and issues. Experience with predictive modeling techniques such as Logistic Regression, GBMs, Elastic Net GLMs, GAMs, Decision Trees, Random Forests, Neural Nets, and Clustering. Proficiency 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 ❯

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 ❯

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 ❯

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 product performance dynamics Delivering the pricing roadmap aligned with company goals Key Skills and Experience: Experience in Personal Lines Pricing is advantageous Knowledge of predictive modeling techniques (e.g., Logistic Regression, GBMs, Decision Trees, Neural Nets) Proficiency in statistical and data science languages (R, Python, SAS, SQL) A quantitative degree (Mathematics, Statistics, Engineering, etc.) Experience with WTW’s 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 ❯

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 ❯

develop, and implement credit scorecards for loan products. Ensure models are unbiased, explainable, and compliant with regulations, promoting fair lending practices. Apply advanced statistical and machine learning techniques (e.g., logistic regression, decision trees, Python, SAS) to model credit risk. Monitor and improve model performance through stress testing and scenario analysis. Contribute strategic insights on credit risk appetite, lending More ❯