6 of 6 Remote/Hybrid Permanent XGBoost Jobs

functions You will have: Strong experience building probability of default or credit scoring models Advanced Python capability Experience with algorithms such as logistic regression, XGBoost, LightGBM or similar A strong understanding of model evaluation (ROC-AUC, Gini, precision/recall) Experience working with complex financial or behavioural datasets You understand ...

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 of the diverse and inclusive community ...

deploying models used in real world decision making (e.g. ensemble methods, regularised regression) Advanced proficiency in R, including tidyverse, data.table, caret or tidymodels, xgboost and Shiny Strong SQL skills with experience querying large scale relational databases such as Azure SQL or PostgreSQL Experience working with high volume or time based ...

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 to use a simple linear model and when to deploy … 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 as spending volatility or income stability—that predict affordability, repayment ...

Research, prototype, and deploy LLM use cases (Q&A, summarisation, document processing) Build and tune models using scikit-learn, PyTorch, TensorFlow/Keras, and XGBoost Create data pipelines for batch and streaming with Spark and Kafka Use Databricks and MLflow for experiments, deployment, and monitoring What you’ll bring Hands ...

continuous refinement of predictive and probabilistic models based on historic and real-time trends. You will guide the implementation of advanced techniques including XGBoost , Random Forest, and MCMC , ensuring model robustness, scalability, and business relevance. You will act as a strategic partner to the pricing and operational teams, driving analytical … learning techniques to real-world datasets, with demonstrable outcomes in business decisioning or operational optimisation. Expertise in advanced modelling approaches such as Random Forests, XGBoost, and Monte Carlo simulations. Proficiency in Python for data manipulation, model development, and deployment. Experience with other languages (e.g. R, Java, Scala) is beneficial, especially ...