51 to 75 of 290 scikit-learn Jobs in the UK

Experience: Proven ability to solve complex, real-world problems through data science and analytics. Experience coaching and reviewing work of junior team members. Strong Python skills (pandas, numpy, scikit-learn) and a solid grounding in probability and statistics. Deep knowledge of machine learning methods and their practical application. Experience managing multiple end-to-end data science projects across More ❯

Experience: Proven ability to solve complex, real-world problems through data science and analytics. Experience coaching and reviewing work of junior team members. Strong Python skills (pandas, numpy, scikit-learn) and a solid grounding in probability and statistics. Deep knowledge of machine learning methods and their practical application. Experience managing multiple end-to-end data science projects across More ❯

Experience: Proven ability to solve complex, real-world problems through data science and analytics. Experience coaching and reviewing work of junior team members. Strong Python skills (pandas, numpy, scikit-learn) and a solid grounding in probability and statistics. Deep knowledge of machine learning methods and their practical application. Experience managing multiple end-to-end data science projects across More ❯

ML development and data science . Deep understanding of AI/ML algorithms , LLMs , GenAI , and automation frameworks. Proficiency with Python, R , and frameworks such as TensorFlow, PyTorch, scikit-learn, NumPy, pandas . Experience in data wrangling , data preprocessing , and statistical modelling. Skilled in data visualisation tools (e.g., Matplotlib, Seaborn, Tableau). Ability to work independently and lead More ❯

ML development and data science . Deep understanding of AI/ML algorithms , LLMs , GenAI , and automation frameworks. Proficiency with Python, R , and frameworks such as TensorFlow, PyTorch, scikit-learn, NumPy, pandas . Experience in data wrangling , data preprocessing , and statistical modelling. Skilled in data visualisation tools (e.g., Matplotlib, Seaborn, Tableau). Ability to work independently and lead More ❯

TensorFlow and PyTorch. Programming: Solid experience in Python and SQL. Experience with R is a nice-to-have. ML and AI: Practical experience using ML modeling libraries like Scikit-Learn, Keras, Tensorflow, PyTorch and similar Generative AI: Some hands-on experience with LLMs for prompt engineering or agents is preferred Cloud Expertise: Building, deploying and monitoring models on More ❯

package Key Responsibilities: Research and deploy LLM-based solutions (e.g., LangChain, Mastra.ai, Pydantic) for document processing, summarization, and clinical Q&A systems. Develop and optimize predictive models using scikit-learn, PyTorch, TensorFlow, and XGBoost. Design robust data pipelines using tools like Spark and Kafka for real-time and batch processing. Manage ML lifecycle with tools such as Databricks More ❯

in data science and AI engineering. Proven track record of delivering AI software and deploying solutions in production. Strong proficiency in Python and libraries such as PyTorch, TensorFlow, Scikit-Learn, Keras, Numpy, Pandas, Langchain, Pinecone, and more. Experience with cloud platforms and compute systems for model training and deployment. Excellent communication and strategic thinking skills. Preferred Experience: Familiarity More ❯

in data science and AI engineering. Proven track record of delivering AI software and deploying solutions in production. Strong proficiency in Python and libraries such as PyTorch, TensorFlow, Scikit-Learn, Keras, Numpy, Pandas, Langchain, Pinecone, and more. Experience with cloud platforms and compute systems for model training and deployment. Excellent communication and strategic thinking skills. Preferred Experience: Familiarity More ❯

in data science and AI engineering. Proven track record of delivering AI software and deploying solutions in production. Strong proficiency in Python and libraries such as PyTorch, TensorFlow, Scikit-Learn, Keras, Numpy, Pandas, Langchain, Pinecone, and more. Experience with cloud platforms and compute systems for model training and deployment. Excellent communication and strategic thinking skills. Preferred Experience: Familiarity More ❯

in data science and AI engineering. Proven track record of delivering AI software and deploying solutions in production. Strong proficiency in Python and libraries such as PyTorch, TensorFlow, Scikit-Learn, Keras, Numpy, Pandas, Langchain, Pinecone, and more. Experience with cloud platforms and compute systems for model training and deployment. Excellent communication and strategic thinking skills. Preferred Experience: Familiarity More ❯

in data science and AI engineering. Proven track record of delivering AI software and deploying solutions in production. Strong proficiency in Python and libraries such as PyTorch, TensorFlow, Scikit-Learn, Keras, Numpy, Pandas, Langchain, Pinecone, and more. Experience with cloud platforms and compute systems for model training and deployment. Excellent communication and strategic thinking skills. Preferred Experience: Familiarity More ❯

using AutoGen, CrewAI , or similar frameworks. Solid understanding of vector embeddings , similarity search, and experience with vector databases. Proficiency in Python and core ML libraries (e.g., PyTorch, TensorFlow, Scikit-learn, Hugging Face). More ❯

deep learning and cutting edge AI & Agents, honed through extensive practical experience across a range of domains Expert-level proficiency in Python and its data science ecosystem (e.g., scikit-learn, pandas), with the ability to select the right tools for complex problems and set technical standards for the team Advanced, hands-on expertise in SQL and big data More ❯

record of shipping models to 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/or AWS) and modern deployment More ❯

CHEP helps move more goods to more people, in more places than any other organization on earth via our 347 million pallets, crates and containers. We employ approximately 13,000 people and operate in 60 countries. Through our pioneering and More ❯

monitoring , and adoption of emerging AI tech. What We're Looking For 5+ years in data engineering, with team or project leadership experience. Advanced Python (Pandas, PyTorch, TensorFlow, Scikit-learn). Strong AWS/GCP , MySQL , and CI/CD experience; Docker/Kubernetes a plus. Excellent communication, organisation, and problem-solving skills. Passion for innovation and ethical More ❯

monitoring , and adoption of emerging AI tech. What We’re Looking For 5+ years in data engineering, with team or project leadership experience. Advanced Python (Pandas, PyTorch, TensorFlow, Scikit-learn). Strong AWS/GCP , MySQL , and CI/CD experience; Docker/Kubernetes a plus. Excellent communication, organisation, and problem-solving skills. Passion for innovation and ethical More ❯

systems in production. What We’re Looking For 3+ years of experience in Data Science. Strong proficiency in Python and experience with ML libraries/frameworks (Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch). Hands-on experience implementing end-to-end ML pipelines (data ingestion, training, validation, serving). Familiarity with ML workflow orchestration tools (Airflow, Prefect, Kubeflow) and More ❯

systems in production. What We’re Looking For 3+ years of experience in Data Science. Strong proficiency in Python and experience with ML libraries/frameworks (Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch). Hands-on experience implementing end-to-end ML pipelines (data ingestion, training, validation, serving). Familiarity with ML workflow orchestration tools (Airflow, Prefect, Kubeflow) and More ❯

with supervised and unsupervised learning, NLP, neural networks, Bayesian statistics, time-series forecasting, collaborative filtering, and deep learning Proficiency in Python and ML libraries (e.g. pandas, numpy, scipy, scikit-learn, tensorflow, pytorch) Experience with cloud platforms (GCP, AWS, Azure) and tools like Dataiku and Databricks Experience with ML Ops, including deployment, monitoring, and retraining pipelines Ability to work More ❯

Terraform for provisioning data infrastructure, including permissions, clusters, jobs, and lakehouse resources. Proven experience in building MLOps pipelines, tracking model lifecycle, and integrating with modern ML frameworks (e.g., scikit-learn, XGBoost, TensorFlow). Exposure to streaming data pipelines (e.g., Kafka, Structured Streaming) and real-time analytics architectures is a strong plus. Experience implementing robust DevOps practices for data More ❯

Terraform for provisioning data infrastructure, including permissions, clusters, jobs, and lakehouse resources. Proven experience in building MLOps pipelines, tracking model lifecycle, and integrating with modern ML frameworks (e.g., scikit-learn, XGBoost, TensorFlow). Exposure to streaming data pipelines (e.g., Kafka, Structured Streaming) and real-time analytics architectures is a strong plus. Experience implementing robust DevOps practices for data More ❯

Terraform for provisioning data infrastructure, including permissions, clusters, jobs, and lakehouse resources. Proven experience in building MLOps pipelines, tracking model lifecycle, and integrating with modern ML frameworks (e.g., scikit-learn, XGBoost, TensorFlow). Exposure to streaming data pipelines (e.g., Kafka, Structured Streaming) and real-time analytics architectures is a strong plus. Experience implementing robust DevOps practices for data More ❯

Terraform for provisioning data infrastructure, including permissions, clusters, jobs, and lakehouse resources. Proven experience in building MLOps pipelines, tracking model lifecycle, and integrating with modern ML frameworks (e.g., scikit-learn, XGBoost, TensorFlow). Exposure to streaming data pipelines (e.g., Kafka, Structured Streaming) and real-time analytics architectures is a strong plus. Experience implementing robust DevOps practices for data More ❯