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.

      •     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.