Senior AI Behavioral Scientist & Product Analyst (10522)

Extreme Networks•Texas, United States, TX

38d•$120,000 - $200,000•Onsite

About The Position

We are seeking an AI Behavioral Scientist and Product Analyst to evaluate and optimize the usability and systems performance of our AI agents and autonomous systems. This role combines deep expertise in human-computer interaction (HCI), user behavior analysis, and systems-level thinking to assess how AI agents perform in real-world contexts and identify opportunities for improvement. The ideal candidate will use quantitative analytics, behavioral science principles, and systems thinking to drive data-informed product decisions and ensure AI systems are both effective and user-centric. Extreme Networks is building Agent ONE, an AI operating model for networking embedded in Extreme Platform ONE. We’re looking for a mid-level AI Product Analyst to own the instrumentation and reporting that prove Agent ONE is working, adopted and valued by customers, and linked to retention and revenue. You’ll sit between Product, Engineering, and UX Research: helping define what to measure, making sure its instrumented right, building the dashboards, and telling the story the data reveals about product success and customer value.

Requirements

Prior experience evaluating AI systems, machine learning models, chatbots, or autonomous agents
4+ years of professional experience in user analytics, product analytics, behavioral research, or HCI research roles
Strong foundation in human-computer interaction (HCI) principles and behavioral science fundamentals (cognitive load, mental models, task completion, user trust, etc.)
Demonstrated proficiency with quantitative analytics: SQL, Python/R for statistical analysis, and familiarity with A/B testing and experimental design
Experience designing and instrumenting analytics tracking, defining success metrics, and interpreting complex datasets
Ability to think systemically about product performance and understand interactions between multiple variables affecting user outcomes
Excellent communication skills with ability to translate technical findings into clear, compelling narratives for non-technical stakeholders

Nice To Haves

Background in formal usability testing, user research, or user testing methodologies (or equivalent research experience)
Experience with analytics platforms (Amplitude, Mixpanel, Looker, Tableau) or BI tools
Familiarity with systems thinking frameworks, causal inference, or econometric modeling
Experience collaborating with ML/AI engineers or understanding of AI/ML capabilities and limitations
Graduate degree in psychology, HCI, behavioral science, statistics, or related field
Experience measuring LLM or conversational-AI systems — evaluation signals such as latency, refusal/quality scoring, or conversation-trajectory classification.
Familiarity with networking, IT operations, or B2B SaaS, and with operational KPIs like MTTR, uptime, or escalation rate.
Experience with a modern data stack (Snowflake, dbt) and light scripting in Python or R.
Exposure to NPS, SUS, or other structured voice-of-customer programs; comfort with sentiment/theme analysis of free-text feedback.

Responsibilities

Design and conduct comprehensive behavioral studies to evaluate AI agent performance across usability, task completion, error recovery, and user satisfaction metrics
Apply HCI and behavioral science frameworks to understand how users interact with AI systems and identify friction points in agent behavior
Assess agent reliability, decision transparency, and failure modes from both user and systems perspectives
Develop user analytics frameworks to track AI agent effectiveness, including KPIs for success, user trust, task completion rates, and cognitive load
Design and implement instrumentation to collect behavioral and interaction data from user sessions
Conduct statistical analysis, A/B testing, and cohort analysis to measure AI agent impact and validate product improvements
Evaluate AI agents holistically across performance dimensions: accuracy, latency, cost efficiency, scalability, and robustness
Model and communicate trade-offs between performance dimensions (e.g., accuracy vs. speed, cost vs. quality)
Identify bottlenecks and systemic issues affecting user experience across the full AI-user interaction lifecycle
Translate behavioral findings and analytics into actionable product recommendations and roadmap priorities
Partner with product managers, engineers, and designers to validate hypotheses and guide feature development
Create dashboards, reports, and narratives to communicate behavioral insights to stakeholders and leadership