Product Analyst – AI

Essential Duties

• Partner with Product Management, Design, and Engineering to define data-informed requirements for new AI and intelligent features.
• Write user stories, define acceptance criteria, and translate model outputs into meaningful user experiences.
• Define and maintain key product health metrics — usage, adoption, retention, and ROI — for AI features and products.
• Conduct deep-dive analyses into user interactions with AI tools (e.g., prompt quality, model relevance, workflow outcomes).
• Collaborate with data scientists and ML engineers to evaluate and monitor model performance, including accuracy, drift, fairness, and edge cases.
• Build dashboards and monitoring systems that track AI system health and highlight actionable insights.
• Perform exploratory data analysis and partner with Product Management teams to identify opportunities for automation, prediction, and decision support across NISC products.
• Benchmark industry AI trends and third-party tools to help shape product direction.
• Promote understanding of NISC’s AI tools, roadmaps, and ethical design principles with internal enablement and support teams.
• Clearly communicate insights and recommendations to technical and non-technical audiences, ensuring alignment with NISC’s mission and Member values.
• Maintain documentation of analytical approaches, ensuring transparency, reproducibility, and explainability in all work.
• Stay informed on AI governance, compliance, and responsible AI practices, helping NISC maintain a “human-in-the-loop” approach across all AI solutions.

Knowledge, Skills & Abilities Preferred

• 3+ years of experience in product analytics, data analytics, or BI — ideally with exposure to AI, ML, or predictive product features.
• Excellent analytical and communication skills — able to translate technical findings into clear, Member-focused recommendations.
• Comfortable working cross-functionally and iteratively in a fast-moving, data-informed environment.
• Passion for responsible, transparent, and explainable AI that improves human work rather than replaces it.
• Strong understanding of machine learning fundamentals — model evaluation, feature importance, overfitting, and drift.
• Experience interpreting model outputs (e.g., predictions, classifications, probabilities) and turning them into actionable product insights.
• Proficiency in SQL and one or more data visualization tools (Tableau, Power BI, Looker, or QuickSight).
• Familiarity with cloud data platforms (AWS, GCP, or Azure) and modern data pipelines.
• Exposure to AI observability tools or techniques (e.g., model drift monitoring, fairness assessment).
• Experience supporting AI product evaluation and governance initiatives.
• Familiarity with utility or broadband industry data or enterprise SaaS environments.
• Prior work on RAG (retrieval-augmented generation) systems or AI Assistant tools a strong plus.