5 of 5 Linear Regression Jobs in England

retrospective analysis, performance analysis, population split analysis and reject analysis. This position will involve performing analysis of multiple data sources using statistical analysis (Logistic Regression, Linear Regression, Decision Trees, Exploratory Data Analysis, CHAID analysis & Cluster Analysis) and computer science to solve business problems. We are looking ...

more than 2 yearsAbility to client face and consultStrong Data Science backgroundStrong understanding of probability and statisticsIdeally Machine learning (experience of techniques such as linear regression, logistic regression, boosting, kNN)Ideally Python or another languageExperience of one or more common frameworks (e.g. Caffe, Theano, Torch, Tensorflow, scikit ...

Step Programming SAS SQL SAS Enterprise Guide and/or SAS Studio Proven experience developing and deploying analytical or predictive models. Strong understanding of: Linear and Logistic Regression Time Series Forecasting Segmentation Models Risk, Pricing, or Predictive Modelling Experience across the full Model Development Lifecycle (MDLC): Development Testing ...

Step Programming SAS SQL SAS Enterprise Guide and/or SAS Studio Proven experience developing and deploying analytical or predictive models. Strong understanding of: Linear and Logistic Regression Time Series Forecasting Segmentation Models Risk, Pricing, or Predictive Modelling Experience across the full Model Development Lifecycle (MDLC): Development Testing ...

which models they chose, the trade-offs, and how they measured success in production Key responsibilities Design, build and validate classical ML models (e.g. regression, tree-based ensembles, clustering) to solve real business problems Select appropriate algorithms and clearly justify model choices to technical and non-technical stakeholders Develop … scientists and product teams to take models from prototype to production Essential skills & experience Classical/traditional ML: Proven experience with models such as linear and logistic regression, decision trees, random forests, gradient boosting (XGBoost, LightGBM), SVMs and clustering. You can explain which models you've used ...