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.