12 of 12 XGBoost Jobs in the City of London

Kinesis, EMR Machine Learning - Supervised Models: Naïve Bayes, Logistic Regression, SVM, Linear Regression, KNN - Tree-Based Models: Decision Trees, Random Forest, Gradient Boosted Trees, XGBoost - Unsupervised Models: K-Means, DBSCAN, Hierarchical Clustering Deep Learning - ANN, CNN, RNN, LSTM - Frameworks: TensorFlow (Gradient Tape), PyTorch NN, Keras Sequential NLP & Generative AI - NLTK ...

with generative models (GANs, VAEs, Diffusion models) Programming & Tools : Expert-level Python programming Proficiency with ML frameworks (PyTorch, TensorFlow, JAX) Experience with scikit-learn, XGBoost, LightGBM Strong software engineering practices and clean code principles Data & Computing : Experience with big data technologies (Spark, Dask) SQL and NoSQL databases Cloud platforms ...

language) and in building data pipelines and ML models using modern libraries across multiple domains: Data science stack: NumPy, pandas/polars, scikit-learn, XGBoost, LightGBM Deep learning: PyTorch, JAX Statistical programming: NumPyro, PyMC Data skills: Proficient in SQL, with the ability to write efficient, maintainable queries and manage data ...

expertise: Skilled in building data pipelines and ML models using modern libraries across multiple domains: Data science stack: NumPy, pandas/polars, scikit-learn, XGBoost, LightGBM Deep learning: PyTorch, JAX Statistical programming: NumPyro, PyMC Data skills: Proficient in SQL, with the ability to write efficient, maintainable queries and manage data ...

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. Contributions to open-source AI projects or research publications. Familiarity with ...

optimisation, marketing optimisation, conversational AI, or related domains. Expert Python and SQL. Strong system design and engineering fundamentals. Hands-on with ML frameworks: sklearn, XGBoost, PyTorch, TensorFlow, LightGBM. Experience with LLMs, prompt design, RAG, evaluation frameworks. Familiarity with data engineering and modern MLOps (registries, pipelines, CI/CD, monitoring). ...

colleagues and share learnings across the team. Requirements: 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 ...

upskilling and development opportunities What You Need Hands-on experience developing and deploying machine learning models in Python using frameworks such as scikit-learn, XGBoost, PyTorch or TensorFlow Experience designing and delivering ML solutions across the full lifecycle, from experimentation to production Strong knowledge of AWS-based ML services (e.g. ...

Mentor junior engineers and contribute to team best practices This role is for you if you have: Strong Python ML experience (e.g. scikit-learn, XGBoost, PyTorch, or TensorFlow) Production experience with AWS ML services (e.g. SageMaker, Lambda, S3) Proven ability to move models from experimentation into live systems Experience with ...

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 ...

series forecasting, ideally in retail demand, workforce planning, or operational intelligence Strong Python skills and familiarity with relevant ML libraries such as scikit-learn, XGBoost, Prophet, or PyTorch Solid understanding of statistical modelling, signal decomposition, and evaluation metrics for time series Experience designing experiments, handling noisy datasets, and improving long ...

model registries Experience: Experience: 3-5 years of professional engineering experience Core ML & Data Science: Proven experience applying traditional ML techniques (Random Forest, XGBoost, Linear/Logistic Regression) to real-world datasets Strong grasp of fundamentals: Transformers, Embeddings, Feature Engineering, and Model Evaluation Generative AI Stack: Deep hands-on experience ...