13 of 13 scikit-learn Jobs in the City of London

mentorship to junior engineers Required Experience & Skills Core Technical Skills Strong hands‐on experience with Python , and at least one of: TensorFlow, PyTorch, scikit‐learn Practical experience with LLMs and Generative AI , including: Prompt engineering Retrieval‐Augmented Generation (RAG) Agentic workflows Experience deploying AI on cloud platforms (Azure ...

stack products Strong backend (Node.js, Python/Flask, FastAPI) and frontend (React, Next.js, Vue) fundamentals ML/data science proficiency: Python, SQL, pandas, scikit-learn, PyTorch/TensorFlow Experience with cloud platforms (AWS, GCP, Azure) and containerisation (Docker, Kubernetes) Proven ability to build data-secure, privacy-compliant infrastructure ...

Machine Learning deployments on AWS, Azure or GCP Extensive hands-on data science experience, leveraging machine learning and data science tools (i.e., pandas, scikit-learn, PyTorch, etc.) Experience with DevOps tools: Kubernetes, Docker, Terraform, CI/CD Pipelines, GitHub/GitLab, GitOps, GitHub Actions, Jira, Jenkins, CircleCI, Datadog ...

enterprise environments Deep understanding of algorithm selection, model complexity trade-offs, and overfitting/underfitting dynamics Strong proficiency in Python and ML ecosystems (scikit-learn, pandas, NumPy) Experience with deep learning frameworks (PyTorch or TensorFlow) Practical knowledge of deep learning architectures (CNNs, RNNs, Transformers) and when to apply ...

full-cycle AI software and deploying production-ready solutions. Strong proficiency in Python and core AI/data science libraries, such as: PyTorch, TensorFlow, ScikitLearn, Keras, NumPy, Pandas PyTorch Geometric, JAX, XGBoost, OpenCV Transformers, LangChain, Pinecone Hands-on experience with cloud computing systems for training, optimising, and deploying models. Strong ...

applications 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 ...

deploying data science workflows at scale Strong grasp of statistical methods and core machine learning algorithms , with practical experience using libraries such as scikit-learn, statsmodels, and the ability to select appropriate models based on data characteristics and problem context. Why Synthesis? Synthesis is known for delivering exceptional ...

Python (for ML development) and JavaScript/TypeScript (for production integration). Deep understanding of ML/DL frameworks such as PyTorch and scikit-learn . Hands-on experience with state-of-the-art foundation models, embeddings, and RAG. Strong knowledge of cloud computing, microservices, API design ...

. Insurance experience Strong technical credibility in ML, AI, and enterprise platforms. Hands-on experience with Azure, Databricks, and ML tools (TensorFlow, PyTorch, Scikit-learn). Knowledge of Responsible AI, ethical frameworks, and compliance standards . Experience in insurance or regulated financial services preferred. Comfortable presenting ...

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 ...

Data/Machine Learning Ops Engineer Location: Erskine, Scotland (Hybrid 2/3 days per week in the office) Candidates must be eligible for clearance. DXC Technology (NYSE: DXC) is a leading independent, end-to ...

This is a unique opportunity to work at the cutting edge of AI, data technology, and elite football analytics. As a Data Scientist, you’ll join a collaborative, high-performing team with a culture rooted ...

Senior Machine Learning/AI Engineer £100,000-£125,000 Hybrid working model: three days per week in the office (Monday, Tuesday, plus one flexible day) LDN 27 days of annual leave plus 8 bank ...