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.