22 of 22 Permanent Feature Engineering Jobs in the North West

design, build, deploy, and optimise machine learning models that power data-driven products and business solutions. This role bridges data science and software engineering, focusing on production-ready ML systems, scalability, and performance. The ideal candidate has strong experience in Python, ML frameworks, data pipelines, and cloud platforms … train, and evaluate machine learning models for prediction, classification, recommendation, or automation use cases. Apply supervised, unsupervised, and deep learning techniques as appropriate. Perform feature engineering, model tuning, and validation to improve accuracy and performance. 2. Productionisation & Deployment Deploy ML models into production using scalable, reliable architectures. Build ...

design, build, deploy, and optimise machine learning models that power data-driven products and business solutions. This role bridges data science and software engineering, focusing on production-ready ML systems, scalability, and performance. The ideal candidate has strong experience in Python, ML frameworks, data pipelines, and cloud platforms … train, and evaluate machine learning models for prediction, classification, recommendation, or automation use cases. Apply supervised, unsupervised, and deep learning techniques as appropriate. Perform feature engineering, model tuning, and validation to improve accuracy and performance. 2. Productionisation & Deployment Deploy ML models into production using scalable, reliable architectures. Build ...

design, build, deploy, and optimise machine learning models that power data-driven products and business solutions. This role bridges data science and software engineering, focusing on production-ready ML systems, scalability, and performance. The ideal candidate has strong experience in Python, ML frameworks, data pipelines, and cloud platforms … train, and evaluate machine learning models for prediction, classification, recommendation, or automation use cases. Apply supervised, unsupervised, and deep learning techniques as appropriate. Perform feature engineering, model tuning, and validation to improve accuracy and performance. 2. Productionisation & Deployment Deploy ML models into production using scalable, reliable architectures. Build ...

design, build, deploy, and optimise machine learning models that power data-driven products and business solutions. This role bridges data science and software engineering, focusing on production-ready ML systems, scalability, and performance. The ideal candidate has strong experience in Python, ML frameworks, data pipelines, and cloud platforms … train, and evaluate machine learning models for prediction, classification, recommendation, or automation use cases. Apply supervised, unsupervised, and deep learning techniques as appropriate. Perform feature engineering, model tuning, and validation to improve accuracy and performance. 2. Productionisation & Deployment Deploy ML models into production using scalable, reliable architectures. Build ...

design, build, deploy, and optimise machine learning models that power data-driven products and business solutions. This role bridges data science and software engineering, focusing on production-ready ML systems, scalability, and performance. The ideal candidate has strong experience in Python, ML frameworks, data pipelines, and cloud platforms … train, and evaluate machine learning models for prediction, classification, recommendation, or automation use cases. Apply supervised, unsupervised, and deep learning techniques as appropriate. Perform feature engineering, model tuning, and validation to improve accuracy and performance. 2. Productionisation & Deployment Deploy ML models into production using scalable, reliable architectures. Build ...

design, build, deploy, and optimise machine learning models that power data-driven products and business solutions. This role bridges data science and software engineering, focusing on production-ready ML systems, scalability, and performance. The ideal candidate has strong experience in Python, ML frameworks, data pipelines, and cloud platforms … train, and evaluate machine learning models for prediction, classification, recommendation, or automation use cases. Apply supervised, unsupervised, and deep learning techniques as appropriate. Perform feature engineering, model tuning, and validation to improve accuracy and performance. 2. Productionisation & Deployment Deploy ML models into production using scalable, reliable architectures. Build ...

advisory or product facing role Education A Bachelor or Master degree in a quantitative field such as Computer Science, Data Science, Statistics, Mathematics or Engineering or Equivalent experience that shows you can deliver results Technical skills Core skills Strong Python and SQL, with clean, maintainable code Solid understanding … fundamentals. For example feature engineering, model selection, handling imbalanced data, choosing and interpreting metrics Experience with PyTorch or TensorFlow GenAI specific Hands on experience with LLM APIs or open source models such as Llama or Mistral Experience building RAG systems with vector databases such as FAISS, Pinecone ...

advisory or product facing role Education A Bachelor or Master degree in a quantitative field such as Computer Science, Data Science, Statistics, Mathematics or Engineering or Equivalent experience that shows you can deliver results Technical skills Core skills Strong Python and SQL, with clean, maintainable code Solid understanding … fundamentals. For example feature engineering, model selection, handling imbalanced data, choosing and interpreting metrics Experience with PyTorch or TensorFlow GenAI specific Hands on experience with LLM APIs or open source models such as Llama or Mistral Experience building RAG systems with vector databases such as FAISS, Pinecone ...

advisory or product facing role Education A Bachelor or Master degree in a quantitative field such as Computer Science, Data Science, Statistics, Mathematics or Engineering or Equivalent experience that shows you can deliver results Technical skills Core skills Strong Python and SQL, with clean, maintainable code Solid understanding … fundamentals. For example feature engineering, model selection, handling imbalanced data, choosing and interpreting metrics Experience with PyTorch or TensorFlow GenAI specific Hands on experience with LLM APIs or open source models such as Llama or Mistral Experience building RAG systems with vector databases such as FAISS, Pinecone ...

advisory or product facing role Education A Bachelor or Master degree in a quantitative field such as Computer Science, Data Science, Statistics, Mathematics or Engineering or Equivalent experience that shows you can deliver results Technical skills Core skills Strong Python and SQL, with clean, maintainable code Solid understanding … fundamentals. For example feature engineering, model selection, handling imbalanced data, choosing and interpreting metrics Experience with PyTorch or TensorFlow GenAI specific Hands on experience with LLM APIs or open source models such as Llama or Mistral Experience building RAG systems with vector databases such as FAISS, Pinecone ...

advisory or product facing role Education A Bachelor or Master degree in a quantitative field such as Computer Science, Data Science, Statistics, Mathematics or Engineering or Equivalent experience that shows you can deliver results Technical skills Core skills Strong Python and SQL, with clean, maintainable code Solid understanding … fundamentals. For example feature engineering, model selection, handling imbalanced data, choosing and interpreting metrics Experience with PyTorch or TensorFlow GenAI specific Hands on experience with LLM APIs or open source models such as Llama or Mistral Experience building RAG systems with vector databases such as FAISS, Pinecone ...

advisory or product facing role Education A Bachelor or Master degree in a quantitative field such as Computer Science, Data Science, Statistics, Mathematics or Engineering or Equivalent experience that shows you can deliver results Technical skills Core skills Strong Python and SQL, with clean, maintainable code Solid understanding … fundamentals. For example feature engineering, model selection, handling imbalanced data, choosing and interpreting metrics Experience with PyTorch or TensorFlow GenAI specific Hands on experience with LLM APIs or open source models such as Llama or Mistral Experience building RAG systems with vector databases such as FAISS, Pinecone ...

real AI projects, learning from experts, and gaining hands-on experience with modern AI tools, models, and data pipelines. Whether you studied Computer Science, Engineering, Maths, Data Science, or a related field, if you have a passion for AI and a hunger to learn — we want to hear from … TensorFlow, PyTorch, or Scikit-learn. Explore & Prepare Data : Help clean, transform, and analyse large datasets for AI applications. Experiment & Research : Test new algorithms, perform feature engineering, and help improve model accuracy and efficiency. Deploy & Monitor Models : Learn how to deploy models into production environments and monitor their performance ...

Generative AI models for predictive analytics and marketing applications. Explore solutions using LLMs, embeddings, and RAG pipelines. Collaborate with data engineers on data quality, feature engineering, and pipeline optimization. Translate business needs into analytical solutions and communicate findings effectively. Requirements Bachelor's or Master's in Data Science ...

Generative AI models for predictive analytics and marketing applications. Explore solutions using LLMs, embeddings, and RAG pipelines. Collaborate with data engineers on data quality, feature engineering, and pipeline optimization. Translate business needs into analytical solutions and communicate findings effectively. Requirements Bachelor's or Master's in Data Science ...

Generative AI models for predictive analytics and marketing applications. Explore solutions using LLMs, embeddings, and RAG pipelines. Collaborate with data engineers on data quality, feature engineering, and pipeline optimization. Translate business needs into analytical solutions and communicate findings effectively. Requirements Bachelor's or Master's in Data Science ...

Generative AI models for predictive analytics and marketing applications. Explore solutions using LLMs, embeddings, and RAG pipelines. Collaborate with data engineers on data quality, feature engineering, and pipeline optimization. Translate business needs into analytical solutions and communicate findings effectively. Requirements Bachelor's or Master's in Data Science ...

Generative AI models for predictive analytics and marketing applications. Explore solutions using LLMs, embeddings, and RAG pipelines. Collaborate with data engineers on data quality, feature engineering, and pipeline optimization. Translate business needs into analytical solutions and communicate findings effectively. Requirements Bachelor's or Master's in Data Science ...

will: Design realistic data science problems grounded in business scenarios Cover the full data science lifecycle, including: Data ingestion & cleaning Exploratory analysis Feature engineering Statistical analysis & modeling Validation and interpretation Implement verified Python solutions Ensure all problems are fully deterministic and reproducible Clearly document: Business context Data inputs ...

will: Design realistic data science problems grounded in business scenarios Cover the full data science lifecycle, including: Data ingestion & cleaning Exploratory analysis Feature engineering Statistical analysis & modeling Validation and interpretation Implement verified Python solutions Ensure all problems are fully deterministic and reproducible Clearly document: Business context Data inputs ...

will: Design realistic data science problems grounded in business scenarios Cover the full data science lifecycle, including: Data ingestion & cleaning Exploratory analysis Feature engineering Statistical analysis & modeling Validation and interpretation Implement verified Python solutions Ensure all problems are fully deterministic and reproducible Clearly document: Business context Data inputs ...

will: Design realistic data science problems grounded in business scenarios Cover the full data science lifecycle, including: Data ingestion & cleaning Exploratory analysis Feature engineering Statistical analysis & modeling Validation and interpretation Implement verified Python solutions Ensure all problems are fully deterministic and reproducible Clearly document: Business context Data inputs ...