6 of 6 XGBoost Jobs in London

natural language processing, computer vision, and recommender systems Strong programming skills in Python, SQL and proficiency with ML libraries (scikit‐learn, pandas, NumPy, XGBoost, LightGBM) Strong analytical and problem‐solving mindset with attention to detail Ability to work independently and drive projects from ambiguous requirements Storytelling with data and insights ...

build trusted relationships across technical and non-technical teams at all levels. Advanced Python skills with strong data science fundamentals (pandas, scikit-learn, XGBoost, statsmodels, or similar). A rigorous, evidence-driven approach to model development with strong problem-solving ability. A commercial mindset - you instinctively connect model performance ...

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 ...

technical and non-technical audiences. Your background Experience with cloud platforms (Azure, AWS, Snowflake). Expertise in ML frameworks and libraries (scikit-learn, XGBoost, PyTorch, TensorFlow). In-depth knowledge of GenAI concepts (LLMs, prompt engineering, RAG, embeddings, vector databases). Full project lifecycle expertise: experimentation, deployment, and ongoing production. ...

production-grade AI solutions in complex, regulated environments Accelerate outcomes using pre-built frameworks and accelerators Core Capabilities AI/ML Engineering: TensorFlow, PyTorch, XGBoost, transformer architectures MLOps & LLMOps: MLFlow, Kubeflow, SageMaker, KServe, vector DBs Cloud & Platform Engineering: AWS, Azure, GCP, Kubernetes, Terraform Data Platforms: Spark, Kafka, Snowflake, Airflow Responsible ...

/CD, Docker, Kubernetes, APIs What you bring Core requirements 5+ years in data science/machine learning Strong foundation in traditional ML (e.g. XGBoost, regression, statistical modelling) Hands-on experience with LLMs/GenAI/NLP/AI systems Experience deploying models into production (not just experimentation) Familiarity with ...