1 to 25 of 138 scikit-learn Jobs in the UK

Conversational AI development Model training, testing and evaluation Prompt Engineering and AI workflow optimisation Programming & Frameworks Strong Python development skills PyTorch TensorFlow Scikit-Learn LangChain LangGraph Hugging Face FastAPI REST APIs Data Technologies SQL NoSQL Databases Data Modelling ETL Development Data Pipeline Engineering Vector Databases such as Pinecone ...

cost efficiency, performance and scalability against standard benchmarks.18. Stay ahead of developments in GenAI (e.g. LLMs, multimodal models), ML frameworks (e.g. PyTorch, TensorFlow, Scikit-learn), orchestration tools (e.g. LangChain, LangFuse), model hubs. Taking responsibility for your own continuous professional development.19. Fostering a culture of responsible innovation and continuous ...

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 ...

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). \n\n \n Experience with data storage for AI, vector databases, semantic search, and knowledge graphs. \n\n \n Contributions ...

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). \n\n \n Experience with data storage for AI, vector databases, semantic search, and knowledge graphs. \n\n \n Contributions ...

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). \n\n \n Experience with data storage for AI, vector databases, semantic search and knowledge graphs. \n\n \n Actively contributes ...

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

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 ...

unstructured data, especially large-scale text or content datasets. Familiarity with common data science and machine learning tools such as Pandas, NumPy, SciPy, Scikit-learn, PyTorch, TensorFlow, or Matplotlib. The ability to translate complex and ambiguous requirements into practical, measurable, data-driven solutions, with strong analytical thinking, problem ...

applications in large scale production environments. Experience managing data science teams and couching team members Proficiency in Python and ML frameworks (PyTorch, TensorFlow, scikit-learn). Hands-on experience with agentic frameworks (LangChain, CrewAI, AutoGen, LangGraph, ADK). Experience with generative models (transformers, GANs/VAEs; diffusion models ...

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 ...

Currently pursuing or recently completed a degree in Data Science, Computer Science, Statistics, Mathematics , or related field Strong foundation in Python (Pandas, NumPy, Scikit-learn) Understanding of machine learning concepts (regression, classification, clustering) Experience with data visualization tools (Matplotlib, Seaborn, or similar) Familiarity with SQL and working with ...

identify 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 ...

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). \n\n \n Experience with data storage for AI, vector databases, semantic search, and knowledge graphs. \n\n \n Contributions ...

feature engineering, model training, evaluation and production deployment, including MLOps, monitoring and drift detection. \n\n \n Practical knowledge of ML frameworks (e.g. scikit-learn, PyTorch, XGBoost) and cloud-native ML services (SageMaker, Azure ML, Vertex AI). \n\n Platform & Cloud Engineering: \n \n Architecture and delivery ...

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 ...

Economics. \nProfessional experience in a data science, ML engineering, or applied data role.Strong Python programming skills, including hands-on experience with pandas, scikit-learn, and at least one ML or deep learning framework.Working knowledge of Bedrock and other AWS managed services.Demonstrable experience building and deploying at least ...

Economics. \nProfessional experience in a data science, ML engineering, or applied data role.Strong Python programming skills, including hands-on experience with pandas, scikit-learn, and at least one ML or deep learning framework.Working knowledge of Bedrock and other AWS managed services.Demonstrable experience building and deploying at least ...

tool/function calling, agentic workflows), including evaluation and safety patterns. Expertise with machine learning frameworks and data science packages (e.g., PyTorch, TensorFlow, Scikit‐Learn, NumPy, Pandas, SciPy, statsmodels). Experience deploying to AWS (e.g., SageMaker, Bedrock) and operating production workloads with attention to cost, performance, security ...

production and supporting them post-deployment. Strong Python programming skills, including object-oriented design and proficiency with key ML libraries (e.g., PyTorch, TensorFlow, Scikit-Learn). Solid understanding of probability and statistical modeling to support robust model development and interpretation. Experience with cloud platforms (especially Azure and/ ...

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 ...

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. ...

Agentic AI and reinforcement learning.* Strong proficiency in programming languages, particularly Python.* Expertise in AI/ML frameworks such as PyTorch/TensorFlow, Scikit-learn, Statsmodels, LangGraph and ADK.* Familiarity with cloud platforms (preferably Google Cloud Platform and Vertex AI).* Strong analytical, critical thinking, and complex problem ...

Agentic AI and reinforcement learning. Strong proficiency in programming languages, particularly Python. Expertise in AI/ML frameworks such as PyTorch/TensorFlow, Scikit-learn, Statsmodels, LangGraph and ADK. Familiarity with cloud platforms (preferably Google Cloud Platform and Vertex AI). Strong analytical, critical thinking, and complex problem ...

Solid understanding of Scrum practices and a strong Agile mindset. Advanced proficiency in Python and its key data and ML libraries (NumPy, PySpark, Scikit learn, TensorFlow/PyTorch) Working knowledge of generative AI models (e.g., ChatGPT, Claude), Databricks GenAI tooling (including embedding models), and modern data structures such ...