201 to 225 of 317 scikit-learn Jobs in the UK

and mentoring a team of talented data scientists. What You’ll Bring 🔥 Deep expertise in machine learning and statistical modelling ⚙️ Skilled with core data science tools (NumPy, Pandas, Scikit-Learn) and deep learning frameworks (PyTorch, TensorFlow) 💡 Proven track record delivering impactful AI/ML projects (e.g. forecasting, recommendation systems, or optimisation) 📈 Experienced project lead with strong technical decision More ❯

and mentoring a team of talented data scientists. What You’ll Bring 🔥 Deep expertise in machine learning and statistical modelling ⚙️ Skilled with core data science tools (NumPy, Pandas, Scikit-Learn) and deep learning frameworks (PyTorch, TensorFlow) 💡 Proven track record delivering impactful AI/ML projects (e.g. forecasting, recommendation systems, or optimisation) 📈 Experienced project lead with strong technical decision More ❯

and mentoring a team of talented data scientists. What You’ll Bring 🔥 Deep expertise in machine learning and statistical modelling ⚙️ Skilled with core data science tools (NumPy, Pandas, Scikit-Learn) and deep learning frameworks (PyTorch, TensorFlow) 💡 Proven track record delivering impactful AI/ML projects (e.g. forecasting, recommendation systems, or optimisation) 📈 Experienced project lead with strong technical decision More ❯

business narratives Contributing to simple, reliable data pipelines and automated quality checks About You Minimum 1 year of commercial Data Science experience Strong hands-on Python (pandas, NumPy, scikit-learn) and SQL Sound understanding of statistics and forecasting Confident communicator - able to explain technical work clearly to non-technical audiences Why Apply Opportunity to build from scratch within More ❯

is highly preferred Demonstrated ability to deploy production-level ML models—particularly in optimisation, demand forecasting, or computer vision Advanced proficiency in Python and ML frameworks such as Scikit-learn, TensorFlow, or PyTorch Solid understanding of supervised and unsupervised learning, statistical modelling, and mathematical optimisation Hands-on experience with AWS services, especially SageMaker, Lambda, and related infrastructure Willingness More ❯

is highly preferred Demonstrated ability to deploy production-level ML models—particularly in optimisation, demand forecasting, or computer vision Advanced proficiency in Python and ML frameworks such as Scikit-learn, TensorFlow, or PyTorch Solid understanding of supervised and unsupervised learning, statistical modelling, and mathematical optimisation Hands-on experience with AWS services, especially SageMaker, Lambda, and related infrastructure Willingness More ❯

a senior data science role. Deep understanding of machine learning model lifecycles and optimisation. Skilled in building, training, and deploying models on large datasets. Proficiency in Python, PyTorch, Scikit-learn, and similar ML frameworks. Experience with cloud environments (AWS preferred), containerisation, and modern data infrastructure. Excellent communication skills, with the ability to work effectively across technical and non More ❯

strong problem-solving skills, a quick learning ability, and enthusiasm for tackling complex challenges. You are proficient in Python, with experience using PySpark and ML libraries such as scikit-learn, TensorFlow, or Keras . You are familiar with big data technologies (e.g., Hadoop, Spark), cloud platforms (AWS, GCP), and can effectively communicate technical concepts to non-technical stakeholders. More ❯

years in data science, with at least 2 years in a leadership role managing teams and projects. Technical Skills : Proficiency in Python, SQL, and machine learning libraries (e.g., scikit learn, TensorFlow, PyTorch). Strong knowledge of cloud environments and services (AWS, Google Cloud). Domain Knowledge : Experience in credit risk modeling, fraud detection, or financial services is highly More ❯

and explore practical applications of AI. Overview of AI and Machine Learning Types of machine learning (supervised, unsupervised, reinforcement) Introduction to Python Libraries for AI - NumPy, Pandas, Matplotlib Scikit-learn for machine learning Building AI Models Data preprocessing Training and evaluating models Advanced Programming for AI Integration This programme aims to equip participants with the knowledge and practical More ❯

NumPy, Pandas, SciPy) • SQL or time-series DBs • Linux & distributed systems expertise • Strong communication & market intuition ✨ Bonus Skills • KDB+/Q, Haskell, or other functional languages • Machine learning (scikit-learn, etc.) • Quant tools & mathematical background • Docker, Kubernetes, AWS 📍 Location : London (1 remote day per week) 💼 Type : Full-time | Competitive comp + performance bonus Ready to build systems that More ❯

NumPy, Pandas, SciPy) • SQL or time-series DBs • Linux & distributed systems expertise • Strong communication & market intuition ✨ Bonus Skills • KDB+/Q, Haskell, or other functional languages • Machine learning (scikit-learn, etc.) • Quant tools & mathematical background • Docker, Kubernetes, AWS 📍 Location : London (1 remote day per week) 💼 Type : Full-time | Competitive comp + performance bonus Ready to build systems that More ❯

NumPy, Pandas, SciPy) • SQL or time-series DBs • Linux & distributed systems expertise • Strong communication & market intuition ✨ Bonus Skills • KDB+/Q, Haskell, or other functional languages • Machine learning (scikit-learn, etc.) • Quant tools & mathematical background • Docker, Kubernetes, AWS 📍 Location : London (1 remote day per week) 💼 Type : Full-time | Competitive comp + performance bonus Ready to build systems that More ❯

methods using the latest AI and data science tools. The ideal candidate will have hands-on experience applying and deploying machine learning and AI tools like Python, TensorFlow, scikit-learn, or similar, to commercial pricing problems. Strong communication skills, modelling ability, and familiarity with data visualisation tools such as Power Bi or tableau is essential. The company offers More ❯

NumPy, Pandas, SciPy) • SQL or time-series DBs • Linux & distributed systems expertise • Strong communication & market intuition ✨ Bonus Skills • KDB+/Q, Haskell, or other functional languages • Machine learning (scikit-learn, etc.) • Quant tools & mathematical background • Docker, Kubernetes, AWS 📍 Location : London (1 remote day per week) 💼 Type : Full-time | Competitive comp + performance bonus Ready to build systems that More ❯

NumPy, Pandas, SciPy) • SQL or time-series DBs • Linux & distributed systems expertise • Strong communication & market intuition ✨ Bonus Skills • KDB+/Q, Haskell, or other functional languages • Machine learning (scikit-learn, etc.) • Quant tools & mathematical background • Docker, Kubernetes, AWS 📍 Location : London (1 remote day per week) 💼 Type : Full-time | Competitive comp + performance bonus Ready to build systems that More ❯

Azure AI Services). Experience with a major conversational AI platform (Google Dialogflow, Amazon Lex, Rasa, or similar). A solid understanding of core Python ML libraries (Keras, scikit-learn, Pandas) and deep learning frameworks (TensorFlow, PyTorch). Desirable (but not essential) experience: Working with tools/interfaces for AI applications e.g. MCP protocol. Training traditional ML and More ❯

Azure AI Services). Experience with a major conversational AI platform (Google Dialogflow, Amazon Lex, Rasa, or similar). A solid understanding of core Python ML libraries (Keras, scikit-learn, Pandas) and deep learning frameworks (TensorFlow, PyTorch). Desirable (but not essential) experience: Working with tools/interfaces for AI applications e.g. MCP protocol. Training traditional ML and More ❯

product teams to operationalise models Strong academic background (STEM or data/AI-related field) 2+ years experience in applied data science or ML Skilled in Python (pandas, scikit-learn; PyTorch/TensorFlow a plus) and SQL Work with a modern AI/ML stack (Python, SQL, Databricks, cloud-first) Hybrid working (2 days/week in Nottingham More ❯

years' experience in applied machine learning and generative AI, including work with large language models. Strong Python programming skills with experience in core ML libraries (numpy, pandas, scikit-learn, boosting methods). Proven ability to design, test, and deploy production-ready machine learning solutions. Hands-on experience in data processing and feature engineering for complex datasets. A degree More ❯

statistical and analytical techniques and concepts sampling methods, Regression Properties of distributions Weighting sample-based data Statistical tests proper usage Real-world applications. Python - NumPy, SciPy, Pandas, MLlib, scikit-learn, and other common data and machine learning related libraries Working knowledge of SQL, data structures and databases (Snowflake - desirable) This is a pragmatic and humble organisation who are More ❯

user experience. Skills & Experience: Strong hands-on experience deploying ML models in production environments. Excellent programming skills in Python and familiarity with ML/DL libraries (TensorFlow, PyTorch, scikit-learn, Pandas). Practical experience with RAG or agentic AI frameworks (LangChain, LlamaIndex). Experience working with LLM APIs (e.g. Hugging Face, OpenAI). Exposure to conversational AI platforms More ❯

user experience. Skills & Experience: Strong hands-on experience deploying ML models in production environments. Excellent programming skills in Python and familiarity with ML/DL libraries (TensorFlow, PyTorch, scikit-learn, Pandas). Practical experience with RAG or agentic AI frameworks (LangChain, LlamaIndex). Experience working with LLM APIs (e.g. Hugging Face, OpenAI). Exposure to conversational AI platforms More ❯

user experience. Skills & Experience: Strong hands-on experience deploying ML models in production environments. Excellent programming skills in Python and familiarity with ML/DL libraries (TensorFlow, PyTorch, scikit-learn, Pandas). Practical experience with RAG or agentic AI frameworks (LangChain, LlamaIndex). Experience working with LLM APIs (e.g. Hugging Face, OpenAI). Exposure to conversational AI platforms More ❯

user experience. Skills & Experience: Strong hands-on experience deploying ML models in production environments. Excellent programming skills in Python and familiarity with ML/DL libraries (TensorFlow, PyTorch, scikit-learn, Pandas). Practical experience with RAG or agentic AI frameworks (LangChain, LlamaIndex). Experience working with LLM APIs (e.g. Hugging Face, OpenAI). Exposure to conversational AI platforms More ❯