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Salary: USD 86,000 - 115,000 / annual
Fanatics Betting & Gaming is hiring a Senior Business Analyst, Operations Automation to own the customer experience analysis for AI agent conversations. This role sits at the intersection of data, product, and customer experience, focusing on understanding why customers get stuck in AI interactions and building the case for fixes.
You will maintain and report key metrics for the customer-facing AI agent—containment, resolution, deflection, escalation rate, repeat contact, and customer satisfaction—at the case and subtype level. You'll build and maintain BI dashboards (Tableau, Looker, Power BI, Sigma, or similar) that Product partners use to track AI performance, and write the data requirements (event definitions, tagging, table structures, data quality checks) that underpin those metrics.
The core of the work is diagnostic: run deep dives into case types and subtypes to find where customers get stuck—where they rephrase questions, loop, abandon, or escalate. Quantify each stall point by frequency, market concentration, and cost in escalations and resolution loss. Pull signals from conversation data, quality scores, customer feedback, behavioral data, and operational metrics into one coherent view. Compare how live agents resolve contacts to how the AI handles the same interactions, identifying gaps in lookups, tools, and actions the AI cannot yet perform.
You'll sort root causes and route them to the right owner—grounding and knowledge content, prompt design, tone, tool access, handoff timing, or policy ambiguity. Where the AI is doing the right thing badly, describe the specific change needed. Work with content, grounding, and engineering partners to get findings specified tightly enough to build and test.
Finally, you'll turn piles of conversations and queries into clear, evidence-backed narratives. Quantify the impact of proposed fixes—contacts avoided, resolution lift, handle time, cost, customer experience—with walkable assumptions. Package findings into business cases and recommendations a Product partner can act on, bring them into intake with your manager, and follow through to measure whether they delivered what you predicted.
You'll work closely with Data, Product, Quality, and CX teams, translating analysis into clear language for technical and non-technical audiences. Much of what you find will be new to the people around you; writing it down so the team can use it is part of the work.
REQUIREMENTS:
- 2+ years in business analytics, data analysis, business operations, customer experience analytics, or closely related role
- Strong SQL: independently pull, join, and validate data across large datasets without waiting on others
- Experience owning recurring reporting and analysis behind a metric, accountable for data quality
- Experience building BI views and dashboards (Tableau, Looker, Power BI, Sigma, or similar) that others use to make decisions
- Ability to move from "the number moved" to "here's the specific root cause, here's roughly what it's worth, and here's what I'd fix." Diagnostic instinct matters more than modeling sophistication
- Comfort working directly in unstructured conversation data—reading transcripts at volume, spotting patterns, and proving them quantitatively rather than stopping at anecdote
- Experience writing up findings and recommendations for partners who don't report to you, with evidence that some were acted on
- Conceptual understanding of how LLM-powered systems behave—prompting, retrieval-augmented generation (RAG), knowledge grounding, and evaluation—enough to distinguish a grounding problem from a prompt problem from a tool gap
- Clear written and verbal communication; ability to write a one-pager someone reads once and understands
- High intellectual curiosity and an AI-first approach to your own work
PREFERRED EXPERIENCE:
- Experience analyzing conversational data from chatbots, voice systems, or agent-assist tools in production
- Comfort with Snowflake, Databricks, or comparable cloud data platform, and with dbt or similar transformation tooling
- Python or R for analysis beyond what SQL handles cleanly
- Background in customer experience, contact center, or support operations analytics—particularly understanding how human agents resolve contacts
- Hands-on experience writing or editing knowledge content, groundings, or prompts for an AI system
- Experience in sports betting, gaming, fintech, payments, or another high-growth, regulated consumer industry
- Experience partnering with ML or Data Science teams on evaluation frameworks, A/B testing, or experiment design
- Familiarity with knowledge base architecture and how content quality drives AI answer quality
