Lead AI Feature Engineer

In DHR Enterprise AI Solutions, you will be responsible for creating reusable, production-grade feature data products that enable high-performing Data Science, Machine Learning and AI solutions across Danaher. You will serve as a technical expert in feature engineering and applied machine learning, helping transform complex enterprise, scientific, laboratory and instrument data into meaningful signals, predictive variables and reusable features that power scalable analytics, ML and AI solutions. Discover, develop and maintain high-quality features from enterprise, scientific, laboratory and instrument data that improve the performance, explainability and reliability of Machine Learning and AI solutions. Design and execute data exploration, feature engineering and feature-selection experiments to identify meaningful signals, validate assumptions and maximise model performance. Build reusable feature data products, feature stores and feature generation pipelines that can be consistently consumed across multiple solutions, models and operating companies. Establish best practices for feature quality, lineage, monitoring, documentation and lifecycle management, ensuring features remain reliable, traceable and production-ready over time. Partner with Data Scientists, AI Scientists, AI Engineers, Product Owners and Architects to translate business and scientific challenges into scalable feature assets and predictive capabilities. Strong hands-on experience in Data Science, Machine Learning, feature engineering and predictive modelling, including the development of high-quality features for production machine learning solutions. Deep understanding of data preparation, feature extraction, dimensionality reduction, feature selection, statistical analysis and model performance optimisation. Proficiency in Python, SQL, notebooks, machine learning libraries and modern data-processing frameworks used to develop and operationalise ML solutions. Experience building scalable data pipelines, feature generation workflows and reusable feature assets in cloud-based environments. Strong analytical and problem-solving skills with the ability to identify valuable signals in complex data and translate them into measurable business and scientific outcomes. Building feature stores, reusable feature repositories or feature engineering frameworks used across multiple machine learning solutions. Developing predictive models using techniques such as gradient boosting, ensemble methods, Bayesian modelling, optimisation algorithms, forecasting or advanced statistical approaches. Working in life sciences, diagnostics, healthcare, laboratory environments, advanced manufacturing or other data-rich scientific industries. It would be a plus if you also possess previous experience in.