Platform Product Manager, AI/ML

Responsibilities

Platform AI/ML Product Strategy

• Define and own the product strategy and roadmap for AI/ML and statistical capabilities as core platform services leveraged across multiple product domains (e.g., CPV, Process Management, Validation, Quality).
• Establish a unified AI/ML platform vision, including reusable models, services, and APIs that can be embedded across the ValGenesis product suite.
• Drive the evolution from fragmented statistical tooling to scalable, cloud-native, AI-powered platform capabilities.
• Identify and prioritize opportunities to apply machine learning, statistical modeling, and generative AI to improve decision-making, automation, and insights across the platform.
• Partner closely with AI/ML engineering and data platform teams to align on architecture, scalability, and long-term technical direction.

Statistical & AI/ML Product Definition

• Act as the subject matter expert (SME) for statistical methods, machine learning, and applied AI within the product organization.
• Define platform-level capabilities for:
• Statistical modeling frameworks (SPC, multivariate analysis, time-series analysis)
• Machine learning services (prediction, classification, anomaly detection)
• Generative AI services (automated insights, narrative generation, copilots)
• Establish standards for:
• Model selection, evaluation, and performance metrics
• Feature engineering and data requirements
• Model explainability and interpretability
• Collaborate with data scientists and ML engineers to translate advanced analytical methods into scalable, reusable product features.
• Define requirements for model lifecycle management, including training, validation, monitoring, and retraining in regulated environments.
• Ensure platform capabilities support compliance with GxP expectations, including auditability, traceability, and validation of AI/ML models.

Platform Architecture & Technical Collaboration

• Partner with engineering on:
• AI/ML platform architecture
• Data pipelines and feature stores
• Model deployment patterns (batch, real-time, hybrid)
• API design for AI/ML services
• Collaborate with UX/UI to ensure complex statistical and AI outputs are translated into intuitive, actionable user experiences.
• Drive consistency and reuse of AI/ML capabilities across products through platform-first design principles.

Cross-Functional Leadership & Stakeholder Engagement

• Serve as the central AI/ML expert bridging Product, Engineering, Data Science, and Go-To-Market teams.
• Engage with customers, data scientists, and technical stakeholders to validate platform capabilities and ensure real-world applicability.
• Support Sales, Customer Success, and Professional Services as the go-to expert on AI/ML and statistical functionality.
• Influence internal teams on best practices for adopting AI/ML capabilities across the product suite.

Go-To-Market & Thought Leadership

• Partner with Product Marketing to articulate the value of ValGenesis AI/ML platform capabilities versus point solutions and legacy statistical tools.
• Monitor industry trends in:
• Applied AI/ML in regulated industries
• Statistical innovation and data science tooling
• Regulatory perspectives on AI/ML in GxP environments
• Contribute to thought leadership through whitepapers, webinars, and customer engagements.

Required Qualifications:

Education

• Bachelor’s or Master’s degree in Statistics, Data Science, Applied Mathematics, Computer Science, or a related quantitative field.
• PhD strongly preferred in Statistics, Machine Learning, or a related discipline.

AI/ML & Statistical Expertise (Core Requirement)

• Deep expertise in statistical methods, including:
• SPC (control charts), process capability analysis
• Regression, ANOVA, DOE
• Multivariate methods (PCA, PLS, MSPC)
• Time-series analysis
• Strong working knowledge of machine learning techniques:
• Supervised learning (regression, classification)
• Unsupervised learning (clustering, anomaly detection)
• Forecasting and probabilistic modeling
• Experience with generative AI and NLP, particularly for automated insights and content generation.
• Hands-on experience with tools such as:
• Python (pandas, scikit-learn, statsmodels)
• R, SAS, or equivalent statistical environments
• Strong understanding of:
• Model validation, performance evaluation, and bias/variance tradeoffs
• Explainability techniques (e.g., SHAP, LIME)
• MLOps concepts (model deployment, monitoring, retraining)

Platform & Technical Experience

• Experience building or defining platform-level capabilities (APIs, shared services, reusable components).
• Familiarity with cloud platforms (AWS, Azure, GCP) and modern data architectures.
• Understanding of data engineering concepts, including pipelines, data quality, and feature engineering.

Regulated Environment Awareness

• Experience working in regulated industries (life sciences preferred but not required).
• Understanding of GxP expectations for software, including:
• 21 CFR Part 11, EU Annex 11
• GAMP 5 / CSA principles for software validation
• Familiarity with challenges of applying AI/ML in regulated environments (traceability, validation, auditability).

Product Management Skills

• 3+ years of product management experience in enterprise SaaS, data platforms, or AI/ML-driven products.
• Demonstrated ability to define highly technical product requirements for data science and engineering teams.
• Experience working in Agile environments with cross-functional teams.
• Strong analytical and strategic thinking with a platform mindset.

Preferred Qualifications

• Prior experience in a platform product management role.
• Experience delivering AI/ML capabilities as part of a SaaS platform.
• Background in life sciences, pharma, or other regulated industries.
• Familiarity with MLOps frameworks and tools.
• Published work, speaking engagements, or recognized expertise in AI/ML or applied statisti