1 to 25 of 112 scikit-learn Jobs in England

mentor engineers and scientists, lead applied research initiatives What You'll Bring Technical Expertise: Strong Python skills with the ML stack: NumPy, pandas, scikit-learn Hands-on experience with at least one deep learning framework (PyTorch, TensorFlow, JAX) Solid grounding in core ML—supervised, unsupervised, evaluation, regularisation, statistics ...

agents). Strong proficiency in Python for AI/ML development, including use of common ML and data libraries (e.g. TensorFlow/PyTorch, scikit-learn, pandas, NumPy). Proven experience integrating AI services with enterprise systems via APIs and event-driven architectures. Practical experience with LLMs, prompt engineering ...

detection. Provide mentorship and technical guidance to junior AI Scientists. Skills: Strong proficiency in Python and experience with frameworks such as NumPy, Pandas, Scikit-learn, and modern Generative AI libraries (e.g., LangChain, LlamaIndex, Azure AI Foundry). Hands‐on experience with PyTorch, leading LLM libraries such as Hugging ...

following: Commercial experience developing AI or Machine Learning solutions Strong Python development skills Experience with Machine Learning frameworks such as TensorFlow, PyTorch or Scikit-learn Experience with Generative AI technologies including LLMs, LangChain or LlamaIndex Hands-on experience building RAG solutions and working with vector databases Experience with ...

Experience Experience building and deploying AI or Machine Learning solutions in a commercial environment Strong Python and SQL skills Experience using TensorFlow, PyTorch, Scikit-learn or similar frameworks Strong understanding of machine learning algorithms, deep learning architectures and model deployment practices Experience with Azure, AWS or GCP cloud ...

successful as an Applied AI Engineer, you should have experience with: Experience using Python and machine learning frameworks such as PyTorch, TensorFlow or scikit-learn to build and deploy scalable solutions. Experience developing advanced AI/ML and GenAI solutions, including working with LLMs, RAG, MCP, agents ...

Community of Practice. Experience Required Essential Strong Python programming and hands-on experience with core ML/AI libraries (for example scikit-learn, TensorFlow, PyTorch). Practical experience developing, training and evaluating machine learning, NLP and predictive models. Experience deploying AI/ML in the cloud, particularly Microsoft ...

Machine Learning Engineer Strong experience developing and deploying Machine Learning models using Python Experience with frameworks such as PyTorch, TensorFlow, Scikit-learn or XGBoost Knowledge of AWS cloud services including SageMaker, Lambda and S3 Experience with MLOps tools such as MLflow, Weights & Biases or Data Version Control Experience ...

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, semantic search, and knowledge graphs. Contributions to open-source AI projects or research ...

leading ML/AI technical initiatives across multiple teams Strong experience with cloud platforms (AWS, Azure, GCP) Experience with ML frameworks (PyTorch, TensorFlow, scikit-learn) and Gen AI/Agentic frameworks (LangGraph, LangChain, VectorDBs, RAG) Understanding of responsible AI practices, including guardrails, hallucination mitigation, and output quality management ...

lifecycle 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 ...

experience across data science and machine learning workflows. Solid understanding of machine learning algorithms and statistical modelling. Experience using ML libraries such as scikit-learn, TensorFlow, or PyTorch. Strong knowledge of data preprocessing, feature engineering, and model evaluation. Experience working with large datasets and applying analytical techniques. Familiarity ...

lifecycle 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). Platform & Cloud Engineering: Architecture and delivery of scalable cloud data ...

Proficiency in SQL including strong knowledge of database structure and efficiency Proficiency in Git Proficiency in AI/ML frameworks: Huggingface, Torch, TensorFlow, Scikit‐Learn, and Spacy Experience in MLOps platforms: MLFlow, dbt, Docker, Kubernetes Experience with cloud environments such as AWS Technical Concept Requirements Experience ...

learning Exposure to machine learning models and corresponding statistics like K‐means, Bayesian, Clustering Strong technical skills with Python along with NumPy, Pandas, Scikit‐Learn Machine learning experience -> predictive and prescriptive modelling Data wrangling and data cleansing experience Strong analytical skills Any experience with Node.JS would be beneficial. ...

Python and SQL, with experience in data manipulation, model development, and analytical workflows. Hands‐on experience with machine learning frameworks such as scikit‐learn, TensorFlow, PyTorch, or similar. Strong experience with cloud platforms (AWS, Azure, or GCP) and building cloud‐native systems. Experience with modern data platforms such ...

science practices tooling governance Essential Skills for the Data Scientist: Strong experience in data science using Python with libraries such as Pandas NumPy scikit-learn or similar Solid grounding in statistics probability machine learning techniques Proven experience working within cloud environments such as AWS Azure or GCP Experience ...

Data Science, Mathematics, Computer Science, or a related field. Strong programming skills in Python or R, with experience using frameworks such as Scikit-learn, PyTorch, or TensorFlow. Experience with cloud platforms or distributed computing (GCP preferred, but AWS or Azure also welcome). Exposure to neural networks ...

bring experience in managing, coaching and developing junior members of a team and wider community. 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 ...

experience in managing, coaching, and developing junior members of a team and wider community. 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 ...

Data Engineering, or Software Engineering roles Strong production Python experience Hands‐on experience training and serving ML models Familiarity with PyTorch, TensorFlow, or scikit‐learn Strong working knowledge of Docker, Linux, Git, and cloud platforms (AWS, Azure, or similar) Experience deploying systems you’ve helped design Excellent communication ...

computer vision projects Knowledge of AWS, Azure, or GCP Familiarity with Docker, APIs, or Git workflowsFamiliarity with frameworks such as PyTorch, TensorFlow, or Scikit-learn Internship, university project, or personal portfolio related to AI/ML What’s on Offer Competitive graduate salary package Company stock options/ ...

track record delivering impactful ML/AI solutions in production. Deep expertise in Python and modern AI/ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn, NumPy, Pandas). Hands-on experience with GenAI, agentic AI, and automated testing for AI systems. Curiosity and creativity to challenge assumptions ...

Senior/Lead roles) Set standards, mentor scientists and lead cross-functional initiatives What You'll Bring Technical Expertise: Strong Python (pandas, scikit-learn, statsmodels) and/or R Solid statistics—inference, experimental design and causal methods Machine learning—supervised and unsupervised methods and rigorous evaluation Strong ...

data and implementing data science processes. Experience in translating customer requirements intotechnical requirements and development objectives. Pythonexpertisecovering standard data sciencelibraries (NumPy, pandas, matplotlib, Scikit-learn,PyTorch, etc),web frameworks (Flask or FastAPI) and visualisation(Dash,Plotly,&Streamlit). Proficient inSQL. Working with unstructured data Building customer and stakeholder ...