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Responsibilities
AI-Native Product Strategy for CPV, CMC & APQR
• Define and own the product roadmap for AI-powered statistical models embedded within CPV, CMC, and APQR workflows.
• Drive the strategy for replacing legacy statistical tools with intelligent, in-platform AI capabilities.
• Identify opportunities to apply generative AI and ML to automate narrative generation, anomaly detection, and predictive batch disposition.
• Partner with the AI/ML team to align data science capabilities with overall product vision and market differentiation.
Statistical & ML Requirements Definition
• Translate regulatory and scientific requirements (FDA Process Validation Stage 3, ICH Q10, EMA CMC guidelines) into precise, actionable data science requirements.
• Define the statistical model selection logic including control charts (Shewhart, CUSUM, EWMA), regression models, PCA, PLS, and multivariate statistical process control (MSPC).
• Specify requirements for ML models, including predictive yield models, anomaly detection, and AI-assisted APQR commentary generation.
• Collaborate with data scientists and ML engineers to define feature engineering, model validation, and explainability requirements for regulated environments.
• Ensure all statistical and AI features meet 21 CFR Part 11, EU Annex 11, and GAMP 5 compliance requirements.
Cross-Functional Collaboration
• Work closely with UX/UI designers to translate complex statistical outputs into intuitive dashboards and visualizations for end users.
• Partner with engineering teams on data pipeline architecture, model deployment patterns, and API design for statistical features.
• Engage directly with customers, quality directors, validation engineers, data scientists, and regulatory affairs leads to conduct discovery and validate requirements.
• Serve as the SME bridge between customer-facing teams (Sales, CSM, Professional Services) and product/engineering on all data science-related features.
Go-To-Market & Competitive Intelligence
• Support product marketing in articulating the differentiation of ValGenesis AI capabilities versus legacy statistical tools and competitor platforms.
• Track industry trends in AI/ML applications for pharmaceutical manufacturing and quality, including regulatory agency guidance on AI in GxP environments.
Required Qualifications:
Education
• Bachelor’s or Master’s degree in Statistics, Biostatistics, Data Science, Chemical Engineering, Pharmaceutical Sciences, or a closely related quantitative field.
• PhD is a strong plus, particularly in statistics, chemometrics, or pharmaceutical engineering.
Domain Expertise
• 5+ years of hands-on experience in the pharmaceutical or biotech industry in a technical role involving statistical process control, process validation, or quality systems.
• Deep working knowledge of CPV Stage 3 requirements under FDA’s 2011 Process Validation Guidance and ICH Q10.
• Strong familiarity with CMC data requirements for regulatory submissions (IND, NDA, BLA, CTD Module 3).
• Experience authoring or reviewing Annual Product Quality Reviews (APQRs) with statistical components.
• Understanding of GxP regulatory frameworks: 21 CFR Part 211, 21 CFR Part 11, EU Annex 11, ICH Q8/Q9/Q10.
Statistical & AI/ML Skills
• Proficiency with statistical methods, including SPC (control charts), process capability indices (Cpk, Ppk), regression analysis, ANOVA, DOE, and multivariate analysis (PCA, PLS, MSPC).
• Working knowledge of machine learning techniques: supervised learning (regression, classification), unsupervised learning (clustering, anomaly detection), and time-series forecasting.
• Familiarity with NLP and generative AI applications particularly for automated report generation and regulatory narrative drafting.
• Experience with statistical software such as JMP, Minitab, SAS, or R; Python experience (pandas, scikit-learn, statsmodels) is a strong advantage.
• Understanding of model validation and qualification in regulated GxP environments (GAMP 5 Category 5 software, CSV/CSA requirements).
Product Management Skills
• 3+ years of product management experience, ideally in life sciences SaaS, regulatory technology, or enterprise data/analytics products.
• Demonstrated ability to write detailed, unambiguous product requirements documents (PRDs) and user stories for technically complex features.
• Experience working in Agile/Scrum development environments; proficiency with tools like Jira, Confluence, ProductBoard, or equivalent.
• Strong analytical thinking with the ability to prioritize competing requirements using data and customer evidence.
Preferred Qualifications
• Prior experience at a life sciences software company in a product or technical role.
• Hands-on experience building or specifying AI/ML features for production SaaS products.
• Knowledge of ISPE PQLI Data Integrity guidance, FDA Computer Software Assurance (CSA) draft guidance, and EU Annex 11 revision (2025 draft).
• ASQ Certified Quality Engineer (CQE), Six Sigma Black Belt, or equivalent statistical quality certification.
• Experience with cloud data platforms (AWS, Azure, GCP) and familiarity with MLOps pipelines.
• Published papers, conference presentations, or technical writing in pharmaceutical statistics or AI/ML in regulated industries.
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