20 of 20 Logistic Regression Jobs

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-learn ...

design, 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 ...

Oracle or similar) to manipulate data and generate insights from large datasets is required. - Demonstrated experience with multiple machine learning techniques, such as logistic regression, decision trees, random forests, clustering, and other predictive modeling methods is required. - Experience working with cloud platforms and data environments, such ...

validation of models for risk management. Advanced experience with the concepts and technologies associated with classical supervised modeling for prediction such as linear/logistic regression, discriminant analysis, support vector machines, decision trees, forest models, etc. Advanced experience with the concepts and technologies associated with unsupervised modeling such ...

validation of models for risk management. Advanced experience with the concepts and technologies associated with classical supervised modeling for prediction such as linear/logistic regression, discriminant analysis, support vector machines, decision trees, forest models, etc. Advanced experience with the concepts and technologies associated with unsupervised modeling such ...

analytics using descriptive, diagnostic, and inferential statistics. Experience with the concepts and technologies associated with classical supervised modeling for prediction such as linear/logistic regression, discriminant analysis, support vector machines, decision trees, forest models, etc. Experience with the concepts and technologies associated with unsupervised modeling such ...

analytics using descriptive, diagnostic, and inferential statistics. Experience with the concepts and technologies associated with classical supervised modeling for prediction such as linear/logistic regression, discriminant analysis, support vector machines, decision trees, forest models, etc. Experience with the concepts and technologies associated with unsupervised modeling such ...

analytics using descriptive, diagnostic, and inferential statistics. Experience with the concepts and technologies associated with classical supervised modeling for prediction such as linear/logistic regression, discriminant analysis, support vector machines, decision trees, forest models, etc. Experience with the concepts and technologies associated with unsupervised modeling such ...

analytics using descriptive, diagnostic, and inferential statistics. Experience with the concepts and technologies associated with classical supervised modeling for prediction such as linear/logistic regression, discriminant analysis, support vector machines, decision trees, forest models, etc. Experience with the concepts and technologies associated with unsupervised modeling such ...

analytics using descriptive, diagnostic, and inferential statistics. Experience with the concepts and technologies associated with classical supervised modeling for prediction such as linear/logistic regression, discriminant analysis, support vector machines, decision trees, forest models, etc. Experience with the concepts and technologies associated with unsupervised modeling such ...

market leading - consumer bureau data, postcode insights, open source data,transactional insights & fraud indicators from partnershipsApply traditional model development techniques and approaches (e.g. logistic regression) as well as adopting new AI/ML techniques such as gradient boosting, random forests, clustering, etc. Utilise the latest cloud based technologies … aboveUse of relevant analytical tooling on a day-to-day basis e.g. Python, SAS, BigQuery, Jupyter Notebook, SQL, etc. Previous experience of developing regression models and scores - e.g. logisticDay to day exposure to large/complex datasets, analytical tools, spreadsheets, analytical platforms, etc. Able to extract data from multiple ...

postgraduate preferred) in a quantitative discipline such as Computer Science, Statistics, Mathematics, Economics, or similar Working knowledge of statistical and machine learning methods (e.g. logistic regression, gradient boosting, random forests, clustering, NLP Proficient in Python and/or R, with strong experience in data quality, model development ...

datasets. Experience working within credit risk, fraud analytics, payments risk, or a related quantitative environment. Practical experience building supervised machine learning models such as logistic regression and gradient boosting models. Ability to work autonomously, solve problems proactively, and thrive in a fast-moving environment. Exposure to clustering, anomaly ...

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 Validation Documentation Production Deployment Advanced ...

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 Validation Documentation Production Deployment Advanced ...

Biostatistics, Change Management, Curious Mindset, Data Governance, Data Literacy, Data Quality, Data Science, Data Visualization, Deep Learning, Generative AI, Graph Algorithms, Learning Agility, Logistic Regression Models, Machine Learning (ML), Machine Learning Algorithms, Neuro-Linguistic Programming (NLP), Pandas Python Library, Python (Programming Language), R Programming, Stakeholder Engagement, Statistical Analysis ...

services) Expertise: Solid understanding of computer science fundamentals and time-series forecasting Machine Learning: Strong grasp of ML and deep learning algorithms (e.g. Logistic Regression, Random Forest, XGBoost, BERT, LSTM, NLP, Transfer Learning) Machine Learning Engineer, XGBoost, GCP, Python, SQL, time-series forecasting Reasonable Adjustments: Respect and equality ...

400.00Skills DesiredArtificial Intelligence (AI), Biostatistics, Curious Mindset, Data Governance, Data Literacy, Data Quality, Data Science, Data Visualization, Generative AI, Graph Algorithms, Learning Agility, Logistic Regression Models, Machine Learning (ML), Machine Learning Algorithms, Neuro-Linguistic Programming (NLP), Pandas Python Library, Python (Programming Language), R Programming, Statistical Analysis, Structured Query ...

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 … 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, why you chose ...

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 … 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, why you chose ...