25 of 25 XGBoost Jobs in London

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 ...

modern deep learning frameworks (e.g. PyTorch, TensorFlow), fine-tuning or distillation of LLMs (e.g. GPT, Llama, Claude, Gemini), machine learning libraries (e.g. scikit-learn, XGBoost). Experience with data storage for AI, vector databases, semantic search and knowledge graphs. Actively contributes to open-source AI projects, research publications and industry ...

with modern deep learning frameworks (e.g.PyTorch, TensorFlow),fine-tuning or distillation of LLMs (e.g. GPT, Llama, Claude, Gemini), machine learning libraries (e.g. scikit-learn,XGBoost). Experience with data storage for AI, vector databases, semanticsearchand knowledge graphs. Actively contributes to open-source AI projects, researchpublicationsand industry events/websites. Familiarity ...

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 ...

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, Markov chains, etc. Experience using specialized machine learning libraries e.g. Fastai, Keras, Tensorflow, pytorch, sci-kit learn, huggingface, etc. Must demonstrate ...

Management, Synapse/Fabric), AWS (Cost Explorer, Athena, Glue), and GCP (BigQuery). AI/ML: proficiency in the Python ML ecosystem (scikit‐learn, XGBoost, Prophet), experience building and deploying models, and familiarity with MLflow or equivalent MLOps tooling. Generative AI: practical experience with LLM APIs (Azure OpenAI, OpenAI ...

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 ...

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 ...

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 ...

deep learning frameworks (e.g. PyTorch, TensorFlow), fine-tuning or distillation of LLMs (e.g., GPT, Llama, Claude, Gemini), and advanced ML libraries (e.g. scikit-learn, XGBoost). Experience with data storage for AI, vector databases, semantic search, and knowledge graphs. Active contribution to open-source AI projects, research publications, and industry ...

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 ...

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 ...

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 ...

related quantitative field. Expert proficiency in GCP’s ML/AI tooling, its data ecosystem, and Python with core ML/AI libraries (PyTorch, XGBoost, TensorFlow). Expert proficiency with SQL development, with Google dialects being advantageous. Proficiency with an API web framework (FastAPI, Django, Flask). Robust CI/ ...

life cycle knowledge (sampling, WoE, scaling) Git and CI/CD workflow experience Hands-on cloud development experience (GCP preferred) Exposure to ML methods (XGBoost, Random Forests, Neural Networks) and traditional stats (Logistic Regression) Awareness of data security, governance, and model risk management standards Nice to Have UK commercial lending ...

team. What We're Looking For Strong commercial experience developing machine learning solutions using Python. Experience with frameworks such as: PyTorch TensorFlow Scikit-learn XGBoost Experience deploying ML solutions on AWS, including services such as SageMaker, Lambda, and S3. Strong understanding of MLOps tooling, including MLflow, Weights & Biases, or Data ...

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 ...

reliable, scalable systems. Essential experience includes: Commercial experience developing and deploying machine learning models in Python Proficiency with ML frameworks such as scikit-learn, XGBoost, PyTorch or TensorFlow Strong experience delivering ML solutions using AWS services (e.g. SageMaker, Lambda, S3) Expertise in experiment design, including hypothesis formulation, A/ ...

proventrack recordof delivering measurable business outcomes Advanced Python and SQL skills Expertise using modern machine learning and statistical modelling frameworks such as scikit-learn, XGBoost and similar tools Experience building, deploying and maintaining predictive models in production environments Strong understanding of experimentation, A/B testing, causal inference and advanced ...

operating models on Databricks. Familiarity with cloud‐based application development (AWS & Azure). Familiarity with one or more ML frameworks and technologies: scikit‐learn, xgboost, TensorFlow, PyTorch, Spark, SageMaker, Vertex AI, Kubeflow, Seldon, Triton. Strong communication skills, able to express ideas clearly and collaborate across teams. #J-18808-Ljbffr ...

refine problem statements and propose solutions considering effort, impact, and scalability trade-offs; Nice to have: Apache Spark, Iceberg, Kafka, dbt, Scikit-Learn, XGBoost, PyTorch, MLFlow, GraphFrames, Ray, AWS S3, EMR, SageMaker, Lakeformation, Terraform, Docker, GitHub CI/CD, Knowledge Graphs, RAG, graph ML, probabilistic programming, A/B testing. ...

projects Requirements: Recent graduates or final year students. Should desirably have knowledge of Artificial Intelligence modeling techniques (logit, GLM, time series, random forests, clustering, XGBoost), statistical programming languages (SAS, R, Python, Matlab), big data tools and platforms (Hadoop, Hive, etc.) and cloud platforms (AWS, Azure, GCP). Solid academic record. ...

identify gaps between predicted vs. actual claim costs Working with large, messy insurance datasets across multiple product lines Tools & tech: Python, SQL, AzureML, XGBoost Opportunity to mentor within the team ...

working software. Experience with any of the following would be an advantage: LLMs AI Agents Graph analytics Behavioural modelling Cyber security Network intelligence PyTorch XGBoost Our technology Python SQL AWS PyTorch Scikit-learn XGBoost Docker Kubernetes Claude Code GitHub Copilot LLMs AI Agents Why Friend MTS? AI is becoming ...

practices (with the freelance Lead Developer) that support Atlas's modelling, forecasting and MMM automation Support productionisation of propensity, forecasting and MMM outputs (e.g. XGBoost, AutoGluon/Chronos, Meridian) built by the Data Scientist, ensuring they run reliably at scale Implement data governance, monitoring, alerting and incident runbooks appropriate … with event streaming architectures (e.g. real‐time ingestion, sub‐second/sub‐minute latency pipelines) Exposure to productionising machine learning or forecasting models (e.g. XGBoost, AutoGluon, Meridian, Robyn) built by data scientists Experience with reverse ETL and activation into media, CRM or ESP platforms Hands‐on experience with Snowflake (warehouse ...