10 of 10 Remote Logistic Regression Jobs

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). Reasonable Adjustments: Respect and equality are core values to us. We are proud ...

Proven experience developing cloud-based machine learning services using one or more cloud providers (preferably GCP) Excellent understanding of classical Machine Learning algorithms (e.g. Logistic Regression, Random Forest, XGBoost, etc.) and modern Deep Learning algorithms (e.g. BERT, LSTM, etc.) Strong knowledge of SQL and Python's ecosystem ...

experience developing cloud‐based machine learning services using one or more cloud providers (preferably GCP). Excellent understanding of classical Machine Learning algorithms (e.g. Logistic Regression, Random Forest, XGBoost, etc.) and modern Deep Learning algorithms (e.g. BERT, LSTM, etc.). Strong knowledge of SQL and Python’s ecosystem ...

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 ...

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 ...

Python skills with a strong foundation in software and data engineering best practices. Deep knowledge of statistical and machine learning models—gradient boosted trees, logistic regression, neural networks, survival analysis, etc. Comprehensive understanding of statistics: hypothesis testing, confidence intervals, bootstrap. Experience with end-to-end model development ...

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 ...

Toolkit Using gradient boosting (e.g. XGBoost, LightGBM, CatBoost) as the default for tabular prediction. Recognising when simpler, more interpretable models, such as regularised logistic regression or well‐constrained decision trees, are the better choice. Applying clustering and segmentation techniques (e.g. k‐means, hierarchical or sequence‐based clustering ...

Applied Machine Learning : Strong background in feature engineering, tabular machine learning, calibrated scoring systems, and production use of models such as gradient‐boosted trees, logistic regression, and related approaches. Content‐Aware Recommendation Systems : Experience combining embeddings, semantic similarity, collaborative signals, and content understanding to build recommendation systems that ...

ideally bring: Commercial experience building credit risk or decisioning models within lending (insurance backgrounds may also be considered) Strong experience with scorecards, logistic regression and/or machine learning models Good understanding of how models influence real lending decisions and commercial outcomes Experience deploying and maintaining models within ...