9 of 9 Permanent Logistic Regression Jobs

experiment, engineer, and deliver innovative predictive models into our modern AWS production environments. The Technical Reality: We operate a pragmatic stack where Linear Regression remains vital for stability and baseline performance, while XGBoost and LLMs are used as responsible additions. We are looking for someone who knows when … discovering good responsible actors and highlighting potential risks from others. Responsibilities Predictive Modelling (Linear & Non-Linear): You will build and maintain foundational Linear Regression models for credit, affordability, and fraud scoring, while developing advanced XGBoost models for deeper risk insights. You will mine data to find behavioural signals—such ...

from "zero-shot" implementations to data-driven optimization. You will instrument data collection, design online evaluation metrics, and deploy simple, effective ML models (e.g., logistic regression, boosted trees) to optimize user outcomes based on real feedback. Be a "Full Stack" Partner: While the backend/data is your ...

role for you. Role and Responsibilities Data science & modelling: Develop, validate, and optimise predictive models using advanced ML algorithms (e.g., gradient boosting, logistic regression, ensemble methods). End-to-end ML engineering: Deploy models as APIs, batch jobs, and streaming services; implement CI/CD, monitoring, and rollback ...

individual research projects. Selects appropriate test formulas and performs computations such as multiple and partial correlation coefficients, t-tests, Chi-square, ANCOVA, linear and logistic regression, multilevel modeling, spatial analyses, interrupted time series models, latent class analysis, principal components analysis, structural equation modeling, multiple imputation, non-parametric analyses ...

from "zero-shot" implementations to data-driven optimisation. You will instrument data collection, design online evaluation metrics, and deploy simple, effective ML models (e.g., logistic regression, boosted trees) to optimise user outcomes based on real feedback. Mentor & Lead: You will work alongside full-stack engineers, upskilling the team ...

from "zero-shot" implementations to data-driven optimisation. You will instrument data collection, design online evaluation metrics, and deploy simple, effective ML models (e.g., logistic regression, boosted trees) to optimise user outcomes based on real feedback. Mentor & Lead: You will work alongside full-stack engineers, upskilling the team ...

from "zero-shot" implementations to data-driven optimisation. You will instrument data collection, design online evaluation metrics, and deploy simple, effective ML models (e.g., logistic regression, boosted trees) to optimise user outcomes based on real feedback. Mentor & Lead: You will work alongside full-stack engineers, upskilling the team ...

from "zero-shot" implementations to data-driven optimisation. You will instrument data collection, design online evaluation metrics, and deploy simple, effective ML models (e.g., logistic regression, boosted trees) to optimise user outcomes based on real feedback. Mentor & Lead: You will work alongside full-stack engineers, upskilling the team ...

from "zero-shot" implementations to data-driven optimisation. You will instrument data collection, design online evaluation metrics, and deploy simple, effective ML models (e.g., logistic regression, boosted trees) to optimise user outcomes based on real feedback. Mentor & Lead: You will work alongside full-stack engineers, upskilling the team ...