Platform Product Manager, AI/ML

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.