1 to 25 of 54 scikit-learn Jobs in the North of England

experience Strong SQL and experience working with large datasets Hands-on cloud experience (Azure, AWS or Snowflake) Expertise in machine learning frameworks (eg scikit-learn, XGBoost, PyTorch, TensorFlow) Strong knowledge of Generative AI, LLMs, RAG, embeddings and evaluation techniques MLOps experience (CI/CD, MLflow, containerisation, monitoring ...

improving models and processes for better performance. Hands‐on development experience in Modelling. Advanced proficiency in Python (including libraries such as NumPy, Pandas, scikit‐learn, PyTorch/TensorFlow). Strong understanding of feature engineering, model selection, evaluation metrics, optimisation techniques and Gen AI. Ability to apply complex statistical ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

experience will you need? Strong experience with Python (core programming language), along with common data science libraries such as Pandas, NumPy and SciKit-Learn Experience applying machine learning and AI techniques (classification, clustering or regression) to real problems, ensuring solutions are robust and meet the intended business outcome ...

experience will you need? Strong experience with Python (core programming language), along with common data science libraries such as Pandas, NumPy and SciKit-Learn Experience applying machine learning and AI techniques (classification, clustering or regression) to real problems, ensuring solutions are robust and meet the intended business outcome ...

experience will you need? Strong experience with Python (core programming language), along with common data science libraries such as Pandas, NumPy and SciKit-Learn Experience applying machine learning and AI techniques (classification, clustering or regression) to real problems, ensuring solutions are robust and meet the intended business outcome ...

engineers, and DevOps, with the freedom to shape how GenAI and ML are used across the platform. Tech stack: Python PyTorch or TensorFlow scikit-learn Spark Kafka Databricks LangChain and RAG-based GenAI systems Cloud-native Azure environment Important Details: Fully remote across ...

engineers, and DevOps, with the freedom to shape how GenAI and ML are used across the platform. Tech stack: Python PyTorch or TensorFlow scikit-learn Spark Kafka Databricks LangChain and RAG-based GenAI systems Cloud-native Azure environment Important Details: Fully remote across ...

engineers, and DevOps, with the freedom to shape how GenAI and ML are used across the platform. Tech stack: Python PyTorch or TensorFlow scikit-learn Spark Kafka Databricks LangChain and RAG-based GenAI systems Cloud-native Azure environment Important Details: Fully remote across ...

Science, Computer Science, Maths, Statistics, Physics, Engineering) or equivalent experience Early career stage (graduate, placement, bootcamp, or personal projects) Strong Python skills (pandas, scikit-learn) Solid SQL skills (joins, aggregations, relational data) Understanding of ML fundamentals (train/test splits, overfitting, evaluation metrics) Clear communication skills Strong learning ...

project initiatives through to completionExtensive industry experience training and shipping production machine learning models.Proficiency with Python, R, Pandas and ML frameworks such as scikit-learn, PyTorch, TensorFlow etcMS in Computer Science, Electrical Engineering, Statistics, Physics, or similar quantitative field.Strong theoretical and mathematical foundations in linear algebra, probability theory ...

Cloud Ecosystems: Hands-on experience securing workloads within AWS or Azure . AI/ML Frameworks: A strong understanding of LangChain, PyTorch, or Scikit-learn is a significant advantage. Experience: 5+ years in Security Engineering, DevOps, or Infrastructure roles. Preferred Qualifications Specialized Certifications: CGLCP (Certified GenAI ...

stack High quality production code in Python . Libraries such as NetworkX , TensorFlow or PyTorch , NLTK, spaCy, Hugging Face, BERT , Pandas, NumPy, scikit learn . Graph based ML familiarity: link prediction, anomaly detection, traversal, community detection. NLP/NLU skills: entity/relation recognition, summarisation, topic modelling, classification ...

integrated into CI/CD workflows. Working knowledge of machine learning concepts, data processing techniques, and common AI/ML frameworks (e.g., PyTorch, Scikit learn, LangChain) - considered a strong advantage. Risk Management and working experience and understanding different delivery methods, e.g. Scrum, Kanban, JIRA, Agile etc. Experience ...

detection logic, and communicate findings effectively to non-technical stakeholders. Essential Skills & Experience Strong Python for data processing and analytics (e.g., pandas, numpy; scikit-learn or equivalent) Structured data expertise: joins, aggregations, data cleaning, handling missing data/outliers, basic data modelling concepts Feature engineering: ability to craft ...

detection logic, and communicate findings effectively to non-technical stakeholders. Essential Skills & Experience Strong Python for data processing and analytics (e.g., pandas, numpy; scikit-learn or equivalent) Structured data expertise: joins, aggregations, data cleaning, handling missing data/outliers, basic data modelling concepts Feature engineering: ability to craft ...

detection logic, and communicate findings effectively to non-technical stakeholders. Essential Skills & Experience Strong Python for data processing and analytics (e.g., pandas, numpy; scikit-learn or equivalent) Structured data expertise: joins, aggregations, data cleaning, handling missing data/outliers, basic data modelling concepts Feature engineering: ability to craft ...

detection logic, and communicate findings effectively to non-technical stakeholders. Essential Skills & Experience Strong Python for data processing and analytics (e.g., pandas, numpy; scikit-learn or equivalent) Structured data expertise: joins, aggregations, data cleaning, handling missing data/outliers, basic data modelling concepts Feature engineering: ability to craft ...

detection logic, and communicate findings effectively to non-technical stakeholders. Essential Skills & Experience Strong Python for data processing and analytics (e.g., pandas, numpy; scikit-learn or equivalent) Structured data expertise: joins, aggregations, data cleaning, handling missing data/outliers, basic data modelling concepts Feature engineering: ability to craft ...