9 of 9 Remote/Hybrid Permanent XGBoost Jobs

Science, Mathematics, Statistics , or a related quantitative field. Scientific Python Ecosystem: Proficiency in Python for data science (pandas, numpy, scipy) and machine learning (sklearn, XGBoost, CatBoost, PyTorch). Data Engineering: Strong experience in SQL and data engineering principles (ETL/ELT), with the ability to handle large, complex datasets independently. ...

libraries such as Pandas, NumPy, and scikit-learn. Experience with frameworks such as TensorFlow, Keras, or PyTorch. Exposure to gradient boosting tools such as XGBoost, LightGBM, or CatBoost. Experience with model deployment tools (e.g., ONNX, TensorRT, TensorFlow Serving, TorchServe). Familiarity with ML lifecycle tools such as MLflow, Kubeflow ...

fundamentals Familiarity with version control (Git) and Agile environments AI, Python, Machine Learning, Software Engineering, Data, APIs, Cloud, Agile, Python, TensorFlow, PyTorch, Scikit-learn, XGBoost, NLTK, OpenAI, LangChain, Pandas, NumPy, MLflow, Kubeflow, Docker, Kubernetes, Azure Machine Learning, Google Vertex ...

client in conjunction with this vacancy only. KEY SKILLS AI, Python, Machine Learning, Software Engineering, Data, APIs, Cloud, Agile, Python, TensorFlow, PyTorch, Scikit-learn, XGBoost, NLTK, OpenAI, LangChain, Pandas, NumPy, MLflow, Kubeflow, Docker, Kubernetes, Azure Machine Learning, Google Vertex ...

from women who are passionate about machine learning and eager to grow: Strong Python skills and experience with ML libraries (pandas, NumPy, scikit-learn, XGBoost, LightGBM, CatBoost, TensorFlow, Keras, PyTorch). Familiarity with model deployment and serving tools (ONNX, TensorRT, TensorFlow Serving, TorchServe). Experience with ML lifecycle tools (MLflow ...

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 ...

Python. • Background in credit risk, rating agency modelling, or customer behaviour modelling. • Confident communicator with senior stakeholders. • Leadership experience in technical teams. • Experience with XGBoost or other boosting techniques. • Knowledge of MLOps or R is a plus but not essential. What They Offer • £120,000 to £150,000 base salary. ...

group with strong influence across risk, product, and strategy. The Role Lead credit decisioning strategy and model development. Stay hands on with modelling using XGBoost, GBM and related techniques. Oversee end-to-end model lifecycle from design to monitoring. Partner with risk, product, and engineering on decisioning and portfolio performance. ...

class classification problems). Work with structured financial transaction data (merchant strings, metadata, timestamps, behavioural features). Apply techniques such as: Gradient boosting (e.g., XGBoost) Tree-based models NLP approaches for merchant text classification Embedding techniques where appropriate Engineer and test new features to improve model accuracy and generalisation. Evaluate … environments. Direct experience working on classification or categorisation problems (ideally transaction, text, or behavioural categorisation). Strong proficiency in Python and ML libraries (e.g., XGBoost, scikit-learn, pandas). Comfortable working with SQL and large datasets (BigQuery experience a strong plus). Experience evaluating model performance rigorously and iterating based ...