Sr. Data Scientist

Requirements Develop statistical and machine learning models for QbD, CPV, PAT, and process monitoring use cases, including CQA modeling, anomaly detection, and multivariate process analysis. Advise product managers and product leaders on how advanced analytics and modeling can create value in the product suite. Collaborate cross-functionally with engineering and domain experts to ensure models are technically sound, explainable, and production-ready. Create prototypes and proofs-of-concept for new analytics-based product features. Ensure modeling practices comply with regulated environments (GxP, GMP). Document modeling approaches, assumptions, limitations, and usage patterns clearly for internal and external stakeholders. Contribute to product discovery by identifying opportunities where AI, ML, or statistical methods can improve usability, decision-making, or automation.