Sr. Data Scientist

Responsibilities

• 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 and where advanced analytics and modeling can create value in our product suite.
• Collaborate cross-functionally with engineering and subject matter experts to ensure models are technically sound, explainable, and ready for production use.
• Create prototypes and proofs of concept to explore new analytics-based product features.
• Ensure all modeling practices align with the expectations of regulated environments (e.g., GxP, GMP).
• Write clear, comprehensive documentation that explains modeling approaches, assumptions, limitations, and usage patterns—for both internal and external stakeholders.
• Contribute to product discovery by identifying opportunities where AI, ML, or advanced statistical methods can improve usability, decision-making, or automation.

Requirements

• This role demands substantial experience in both data science and life sciences manufacturing domains. Candidates must demonstrate at least 5 years of hands-on experience in each of the following areas:


• 1. Advanced Data Science / Statistical Modeling
• Proven experience applying data science and statistical modeling in process manufacturing, life sciences, or industrial settings.
• Expertise with tools and languages such as SIMCA, JMP, MODDE, Design Expert, Python, R, or MATLAB.
• Strong working knowledge of multivariate analysis, chemometrics, predictive modeling, time-series modeling, or hybrid modeling techniques.


• 2. QbD, CPV, PAT, or Process Validation in Regulated Environments
• Deep understanding of Quality by Design, Continued Process Verification, Process Analytical Technology, or process validation frameworks.
• Practical experience working with real manufacturing or bioprocessing data under GxP, GMP, or other regulatory constraints


• Interpersonal & Collaborative Skills
• Product Thinking: You understand how your models and analytics connect to business outcomes and customer workflows. You think in terms of features, value, and usability—not just model performance.
• Inquisitive and Outcome-Oriented: You ask questions that get to the heart of what users need, and you enjoy solving complex, ambiguous problems in real-world settings.
• Strong Communicator: You can explain statistical modeling choices in plain language—both to product teams focused on customer outcomes and to engineers building system functionality.
• Collaborative: You partner well with product managers, engineers, and domain experts to co-create practical, reliable, and user-aligned solutions.
• Rigorous: You document your work with precision and transparency so that it’s understandable, repeatable, and traceable—especially in regulated environments.