Lead AI Feature Engineer

Accountabilities:: Discover, develop, and maintain high-quality features from enterprise, scientific, laboratory, and instrument data to improve the performance, reliability, and explainability of ML and AI solutions. Design and execute data exploration, feature engineering, and feature-selection experiments to identify meaningful signals, validate assumptions, and optimize model performance. Build reusable feature data products, feature stores, and feature-generation pipelines that can support multiple models, solutions, and business environments. Establish and promote best practices for feature quality, lineage, monitoring, documentation, and lifecycle management. Ensure feature assets remain reliable, traceable, scalable, and production-ready throughout their lifecycle. Partner with Data Scientists, AI Scientists, AI Engineers, Product Owners, and Architects to translate complex business and scientific challenges into reusable feature assets and predictive capabilities. Apply statistical and machine learning techniques to identify valuable patterns and signals within complex datasets. Contribute to scalable data-processing and feature-engineering practices that enable high-performing analytics, machine learning, and AI solutions. Requirements: Strong hands-on experience in Data Science, Machine Learning, feature engineering, and predictive modeling , including developing production-quality features for machine learning solutions. Deep understanding of data preparation, feature extraction, dimensionality reduction, feature selection, statistical analysis, and model performance optimization . Strong proficiency in Python and SQL , along with experience using notebooks, machine learning libraries, and modern data-processing frameworks. Experience building scalable data pipelines, feature-generation workflows, and reusable feature assets in cloud-based environments. Strong analytical and problem-solving abilities, with the ability to identify valuable signals within complex data and translate them into measurable outcomes. Ability to collaborate effectively with multidisciplinary teams and communicate technical concepts clearly to technical and business stakeholders. Experience building feature stores, reusable feature repositories, or feature-engineering frameworks is a plus. Experience with predictive modeling approaches such as gradient boosting, ensemble methods, Bayesian modeling, optimization algorithms, forecasting, or advanced statistical techniques is desirable. Experience working with life sciences, diagnostics, healthcare, laboratory environments, advanced manufacturing, or other data-rich scientific industries is advantageous. Benefits: Full-time position based in India. Remote work arrangement , allowing eligible employees to work from home. Comprehensive and competitive employee benefits programs. Healthcare benefits designed to support employee well-being. Paid time off. Flexible working arrangements for eligible roles. Opportunity to work on production-grade AI, machine learning, and data products with enterprise-scale impact. Exposure to multidisciplinary teams across Data Science, AI Engineering, Product, Architecture, and Data Engineering. Opportunity to work with complex scientific, laboratory, enterprise, and instrument data. Professional growth opportunities within a global technology and science-focused environment.