Define and execute the product strategy for Neon One’s Data & Intelligence platform, including data, insights, and agentic AI products
Translate company and customer objectives into measurable product outcomes supported by a now-next-future rolling roadmap
Evangelize a unified vision for data, analytics, and AI within Neon One’s portfolio, ensuring alignment across teams and stakeholders
Identify and prioritize strategic opportunities for LLM, NLP/NLU, RAG, and machine learning integrations that deliver measurable nonprofit impact
Manage a prioritized milestone backlog for the data and intelligence domain that balances innovation with delivery
Partner with data scientists, ML engineers, Product managers, and research teams to define AI-driven features aligned with business and user needs
Establish model evaluation criteria (accuracy, recall, F1, AUROC, MSE, bias detection, etc.), rules, performance SLAs, and monitoring thresholds
Identify new and supplemental data sources to drive effective predictive and prescriptive models
Support prompt and context engineering, ensuring high-quality datasets, traceability, and iterative model optimization
Work with engineering to design data pipelines, governance frameworks, feedback loops, and API integrations that improve model performance and scalability
Champion responsible AI principles such as bias mitigation, transparency, auditability, and explainability throughout the product lifecycle
Requirements
5-7+ years of product management experience in SaaS, data analytics, or AI/ML-driven platforms (nonprofit or adjacent sectors preferred)
Proven experience defining and launching AI-powered or data-driven solutions, including LLM, NLU/NLP, ML, or RAG-based systems
Hands-on experience with model evaluation frameworks (e.g., MAE, AUROC, F1, confusion matrix)
Demonstrated success working cross-functionally with engineering, data science, and go-to-market teams
Experience with Agile or Lean product development methodologies
Comfort with experimentation (A/B testing, beta programs, data-informed iteration)
Exposure to nonprofit, fundraising, or donor management technologies strongly preferred
Bachelor’s degree in Computer Science, Engineering, Data Science, or related field (or equivalent practical experience)