9 of 9 DemandTrendPermanent scikit-learn Jobs in the East of England

algorithms and techniques (supervised, unsupervised, reinforcement learning). Solid background in data preprocessing, wrangling, and feature engineering. Proficiency in Python (essential) and familiarity with relevant libraries (NumPy, pandas, scikit-learn, TensorFlow, PyTorch). Experience with prompt engineering and model evaluation. Deployment experience using Docker or other containerisation tools. Exposure to GPU-based environments for large-scale model training More ❯

algorithms and techniques (supervised, unsupervised, reinforcement learning). Solid background in data preprocessing, wrangling, and feature engineering. Proficiency in Python (essential) and familiarity with relevant libraries (NumPy, pandas, scikit-learn, TensorFlow, PyTorch). Experience with prompt engineering and model evaluation. Deployment experience using Docker or other containerisation tools. Exposure to GPU-based environments for large-scale model training More ❯

algorithms and techniques (supervised, unsupervised, reinforcement learning). Solid background in data preprocessing, wrangling, and feature engineering. Proficiency in Python (essential) and familiarity with relevant libraries (NumPy, pandas, scikit-learn, TensorFlow, PyTorch). Experience with prompt engineering and model evaluation. Deployment experience using Docker or other containerisation tools. Exposure to GPU-based environments for large-scale model training More ❯

related field. 5+ years' experience in data science, with proven success delivering end-to-end projects. Proficiency in Python, R, SQL, and machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn). Strong skills in data storytelling, problem-solving, and working with both structured and unstructured data. Experience with APIs, cloud platforms (especially AWS), and MLOps practices is a More ❯

leadership or architecture role, ideally with some experience in leading a Centre of Excellence or a similar initiative. • Hands-on experience with Al frameworks such as TensorFlow, PyTorch, Scikit-learn, and cloud platforms like AWS, Azure, or Google Cloud. • Experience in multiple industries is advantageous (e.g., healthcare, finance, retail). Skills: AI/ML Expertise: Strong understanding of More ❯

papers in Generative AI. Strong experience in designing and building machine learning and deep learning models to solve complex business problems. Proficiency in Python and libraries such as Scikit-learn, TensorFlow, Keras, and PyTorch. Solid foundation in statistics, probability, and core ML algorithms (regression, decision trees, gradient descent, SVMs, etc.). Hands-on experience with transformer architectures and More ❯

be joining a small, agile team working on projects ranging from genomic data interpretation to clinical trial optimization . Key Responsibilities: Build and deploy ML models using Python , scikit-learn , and PyTorch Work with structured and unstructured biomedical datasets (e.g. genomic sequences, patient records, imaging) Collaborate with bioinformaticians and clinical researchers to translate data into actionable insights Contribute More ❯

publications and preprints. Strong breadth and expertise in Statistical analysis, machine learning, data visualization, programming (Python, R, etc.), data cleaning and data manipulation. Tools: Python, R, SQL, TensorFlow, Scikit-learn, Tableau, Power BI. Ability to generate high quality ideas and be self-driven to explore. Strong experience in programming and comfortable modifying existing code-base. Experience with Python 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 ❯