3 of 3 Feature Engineering Jobs in the East of England

datasets (e.g. genomic sequences, patient records, imaging) Collaborate with bioinformaticians and clinical researchers to translate data into actionable insights Contribute to the development of internal tools for data preprocessing , feature engineering , and model evaluation Requirements: 3+ years of experience in data science or ML, ideally in biotech or healthcare Strong Python programming skills and experience with ML libraries More ❯

datasets (e.g. genomic sequences, patient records, imaging) Collaborate with bioinformaticians and clinical researchers to translate data into actionable insights Contribute to the development of internal tools for data preprocessing , feature engineering , and model evaluation Requirements: 3+ years of experience in data science or ML, ideally in biotech or healthcare Strong Python programming skills and experience with ML libraries More ❯

Key Responsibilities: Design, develop, and deploy AI/Machine learning models and solutions, including LLMs and GenAI. Fine-tune and evaluate open-source LLMs, applying techniques such as prompt engineering and model re-tuning. Work with a variety of structured and unstructured datasets, handling preprocessing, cleaning, and feature engineering. Develop pipelines for creating, preparing, and optimising data for … business needs. Document workflows, data pipelines, and model processes for knowledge transfer and reproducibility. Key Skills & Experience: 4-5 years' experience across AI/ML, data science, or data engineering, with recent hands-on work in GenAI. Proven experience fine-tuning and deploying open-source LLMs. Strong knowledge of AI/ML 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 and tuning. Experience with big data tools More ❯