1 to 25 of 33 XGBoost Jobs in London

LangChain/LangGraph or Hugging Face. Strong Python and SQL, with working knowledge of the wider ML stack (pandas, scikit-learn, PyTorch or TensorFlow, XGBoost). Experience with cloud data and ML platforms, ideally AWS (S3, SageMaker Pipelines, Glue) and/or GCP, and MLOps practices (MLflow, CI/ ...

Strong expertise in Python and SQL. Deep familiarity with the Python data stack (pandas, NumPy, scikit-learn) and standard ML frameworks (such as PyTorch, XGBoost, or LightGBM). Engineering Rigor: Proven ability to build for production. Experience working in cloud-based environments (AWS or GCP) and familiarity with CI/ ...

expertise: feature engineering, model training, evaluation and production deployment, including MLOps, monitoring and drift detection. Practical knowledge of ML frameworks (e.g. scikit‐learn, PyTorch, XGBoost) and cloud‐native ML services (SageMaker, Azure ML, Vertex AI). Architecture and delivery of scalable cloud data and AI platforms on AWS, Azure ...

Anaplan Applications, translating raw customer data into automated, high-value business insights. Advanced Modeling & Timeseries Forecasting: Design, train, and implement robust Machine Learning (LightGBM, XGBoost) and Deep Learning (PyTorch) models specifically focused on complex demand sensing and resolving cold-start forecasting problems. Feature Engineering Pipelines: Build and optimize scalable data ...

strategic AI solutions. Software/Data Engineering: Python proficiency, API design, SQL, data integration, and ML/AI libraries (PyTorch, TensorFlow, scikit-learn, XGBoost, Hugging Face Transformers). Agentic AI Systems: Hands-on experience with agentic AI setups including LLM orchestration, tool use/function calling, RAG, agents, prompt engineering ...

with machine learning libraries such as scikit-learn, TensorFlow, or PyTorch. Practical knowledge of algorithms and techniques such as regression, decision trees, random forest, XGBoost, k-means, anomaly detection, and time series forecasting. Working understanding of model evaluation and explainability concepts, e.g. overfitting, bias, cross-validation. Experience developing and implementing ...

SQL. Expert-level experience with the Python data stack (pandas, NumPy, scikit-learn) and at least one major ML framework (such as PyTorch or XGBoost/LightGBM) Systemic Problem Solving: Ability to work on unique issues requiring conceptual thinking and broad impact. You know how to build for long-term ...

skills and experience working with large-scale relational and cloud-based databases (e.g., Snowflake,BigQuery). Experience with machine learning frameworks (e.g., scikit-learn,XGBoost, TensorFlow,PyTorch). Ability to work autonomouslyand comfortable with ambiguity. Experience in the healthcare field, preferably in generic pharmaceuticals (pricing,sourcingor distribution) preferred, though notrequired. ...

Machine Learning systems from research all the way to deployment and maintenance Excellent Python programming skills and hands on experience with libraries such as XGBoost, PyTorch, TensorFlow, or Hugging Face Experience working with cloud‐based data and AI platforms, particularly Azure and Databricks Strong stakeholder engagement and storytelling capabilities; able ...

time data is a strong plus Also desirable: experience with vector databases (pgvector, Pinecone, Weaviate, Qdrant or similar); machine learning experience (PyTorch, TensorFlow, XGBoost/LightGBM) for building, maintaining and extending existing models; Bloomberg API integration to Python; data science/statistics, signal research, backtesting or factor modelling; full stack ...

will have experience in many of the following: Hands-on experience developing and deploying ML models in Python using frameworks such as scikit-learn, XGBoost, PyTorch, or TensorFlow Strong experience with AWS ML services (SageMaker, Lambda, S3) in production environments Strong experiment design skills: hypothesis formulation, A/B testing ...

will have experience in many of the following: Hands-on experience developing and deploying ML models in Python using frameworks such as scikit-learn, XGBoost, PyTorch, or TensorFlow Strong experience with AWS ML services (SageMaker, Lambda, S3) in production environments Strong experiment design skills: hypothesis formulation, A/B testing ...

cards, or BNPL. Deep proficiency in PD model development and validation, with strong knowledge of calibration techniques. Advanced Python and SQL skills; familiar with XGBoost, scikit-learn, pandas, MLFlow. Experience with explainability frameworks such as SHAP, LIME, PDP. Ability to communicate technical concepts clearly and influence cross-functional decisions. Familiarity ...

opportunities to improve business efficiency through intelligent automation. Skills & Experience Strong Python programming skills with experience using libraries such as Pandas, NumPy, Scikit-learn, XGBoost or LightGBM, and Plotly, Rust, Typescript, Python or Matplotlib. Experience developing machine learning models for prediction, optimisation or classification. Strong SQL skills and experience working ...

science, machine learning, pricing analytics, or commercial operations. Strong technical background in Python or R, SQL, and applied ML libraries (e.g. scikit-learn, TensorFlow, XGBoost). Experience with data visualization tools (Tableau, Power BI, or similar). Understanding of statistical methods, supervised/unsupervised learning, and business experimentation. Ability ...

also unstructured data eg. Text, PDFs, jpgs, call recordings, video,etc. Knowledge of machine learning modelling techniques and how to fine-tunethosemodels eg. XGBoost, Deep Neural Networks, Transformers, ResNets,VAEs, GANs,Markov chains, etc. Experience using specialized machine learning libraries eg. Fastai, Keras, Tensorflow, pytorch, sci-kit learn, huggingface, etc. ...

unstructured data eg. Text, PDFs, jpgs, call recordings, video, etc. Knowledge of machine learning modelling techniques and how to fine-tune those models eg. XGBoost, Deep Neural Networks, Transformers, ResNets, VAEs, GANs, Markov chains, etc. Experience using specialized machine learning libraries eg. Fastai, Keras, Tensorflow, pytorch, sci-kit learn, huggingface ...

Airflow, Prefect, etc.). Hands-on experience with common ML libraries and frameworks, e.g. PyTorch, scikit-learn, and gradient-boosting libraries such as XGBoost or LightGBM. Clear evidence of practical experience defining and deploying containerised systems, i.e.: Implementing APIs for internal services, e.g. via FastAPI; Deploying containerised systems to production ...

platforms.Model Development, Governance & Explainability* Deep experience applying a wide range of machine learning techniques to real-world business problems, including using models such as XGBoost and deep learning approaches using frameworks such as PyTorch or TensorFlow, taking solutions from experimentation through to production deployment and operation.* Strong knowledge of model ...

kept working. Statistics and experimentation: hypothesis testing, A/B testing, power and sample sizing. Classical ML: classification and ranking with scikit-learn and XGBoost or LightGBM, evaluated rigorously: validation design, calibration, leakage. Decision science: applied work with at least one of contextual bandits, causal inference or uplift modelling ...

Hands-on experience building credit scorecards or risk models (application, behavioural, or collections) Proficiency in Python and SQL; experience with ML frameworks (scikit-learn, XGBoost, etc.) Familiarity with Open Banking data, bureau data (Experian, Equifax, TransUnion), or alternative data sources Comfort working in small, fast-moving teams where you have ...

ranking, NLP, or LLMs).• Data & ML Ecosystem: Familiarity with data processing libraries (e.g. Pandas, NumPy) and ML frameworks (e.g. PyTorch, Scikit-learn or XGBoost).• Growth Mindset: A genuine interest in distributed system and software design, concurrent algorithms, data structures, and software engineering.At Quantcast, we craft offers that reflect ...

ensemble techniques (e.g., Gradient Boosted Trees, Random Forests).Core Technical Proficiency: High proficiency in Python for data science (including libraries like Pandas, Scikit-Learn, XGBoost/LightGBM) and SQL.Large-Scale Data Expertise: Demonstrable experience working with very large, complex, and sometimes messy datasets to build effective models. Experience with geospatial ...

/Senior) to analyse longitudinal healthcare datasets and build predictive models. The role emphasizes Python/SQL proficiency, ML libraries (scikit-learn, pandas, XGBoost) and NLP transformer techniques, in a fast-moving, cross-functional team. You will validate and deploy models on real clinical data, address missing values and class ...

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 for data analysis (Jupyter, Pandas, Scikit Learn ...