Overview

Our 60,000+ associates work across the globe at more than 15 unique businesses within life sciences, diagnostics, and biotechnology.

Responsibilities

• 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.