Senior AI Behavioral Scientist & Product Analyst (10522)

Extreme Networks · United States · $110,000–$120,000 (stated in the posting)

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Over 50,000 customers globally trust our end-to-end, cloud-driven networking solutions. They rely on our top-rated services and support to accelerate their digital transformation efforts and deliver unprecedented progress.

Become part of something big with Extreme! As a global networking leader, learn why there is no better time to join the Extreme team.

Position details

Title of position:  Senior AI Behavioral Scientist & Product Analyst

Position type: Full time

Location:  Austin, TX

Position reports to: Senior Director AI Product Foundations Engineering

Application deadline: Applications are being accepted on a rolling basis and this posting will remain open until filled.

Work authorization: We are unable to sponsor or take over sponsorship of an employment visa, including H-1B visas, at this time 

Scope:

• 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.

Primary Responsibilities:

AI Agent Evaluation & Behavioral Analysis

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

Analytics & Quantitative Research

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

Systems-Level Performance Assessment

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

Product Insights & Strategy

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

Required Qualifications

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

Preferred Qualifications:

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.

Key Compentencis:

• Analytical Thinking: Ability to break down complex problems, identify patterns, and derive insights from data

  User Empathy: Deep understanding of user needs, motivations, and behavioral patterns; ability to advocate for user-centric design

  Systems Thinking: Comfort working with interconnected systems and understanding emergent behavior and trade-offs

  Curiosity: Passion for understanding how and why users interact with technology; willingness to challenge assumptions

  Communication: Ability to translate complex behavioral and technical findings into accessible insights for diverse audiences

  Collaboration: Ability to work cross-functionally with product, engineering, and design teams to drive product improvements

Equal Employment Opportunity

1. Extreme Networks, Inc. is an Equal Opportunity Employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran. We are committed to taking affirmative action to employ and advance in employment qualified protected veterans, including disabled veterans, recently separated veterans, active-duty wartime or campaign badge veterans, and Armed Forces service medal veterans.
2. Extreme Networks also strives to prevent other, subtler forms of inappropriate behavior (for example, stereotyping) from ever gaining a foothold in our organization. Whether blatant or hidden, barriers to success have no place at Extreme Networks. We encourage people from underrepresented groups to apply.

Fair chance and background checks

• Extreme Networks will consider qualified applicants with criminal histories in a manner consistent with the California Fair Chance Act, Los Angeles Fair Chance Initiative for Hiring Ordinance, Los Angeles County Fair Chance Ordinance for Employers, Philadelphia Fair Criminal Record Screening Standards Ordinance, Illinois Human Rights Act, Cook County Human Rights Ordinance, Seattle Fair Chance Employment Ordinance, and the San Francisco Fair Chance Ordinance. An applicant's conviction history will not be considered until after a conditional offer of employment has been made. Following any individualized assessment, applicants will be provided with the opportunity to respond before any adverse action is taken.
• Extreme Networks does not seek salary history information from applicants and will not rely on salary history in determining whether to offer employment or in setting compensation.

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