5 of 5 Logistic Regression Jobs

Role Mandate: Lead the full scorecard lifecycle , from development and validation of logistic regression models through to implementation, testing and automated monitoring Own credit policy and credit reference agency analytics , incorporating FCA/governance requirements and leading senior stakeholder engagement. Ideal candidate Proven modelling/scorecard lead with … strong project delivery, transformation and stakeholder skills Must have Large scale analytics experience such as in a Banking/Telco environment SAS coding including logistic regression and macro proficiency Scorecard development and policy rule , experience across large portfolios Strategy development , implementation and testing skills Monitoring, development and automation ...

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 ...

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 ...

comfortably in notebooks, but can also write a plain .py script that runs on a schedule Familiar with scikit-learn or stats models for regression and forecasting Statistics and modelling Solid descriptive statistics, and the judgement to know when the mean is misleading Understands sampling, bias, correlation vs causation … confidence intervals Can run and interpret a hypothesis test or an A/B comparison Linear and logistic regression — fit one, and explain what the coefficients mean in plain English Knows when not to model, and will say “the data can't answer that” Other Data Skills ● Apply ...