Senior Business Analyst, Operations Automation

Fanatics Betting & Gaming•Denver, CO

1d•$86,000 - $115,000

About The Position

Fanatics Betting & Gaming is hiring a Senior Business Analyst, Operations Automation, to analyze customer experience in AI agent conversations. The role owns detailed analysis of customer-facing AI performance, including containment, resolution, deflection, escalations, repeat contacts, and customer satisfaction at the case and subtype level. The analyst will inspect conversation data, identify where customers get stuck, determine root causes, quantify business impact, and recommend actionable improvements to Product and operational partners. The role also establishes measurement for new AI surfaces, maintains BI reporting and dashboards, defines data requirements with Data partners, and monitors data quality. It compares AI handling with live-agent resolution to identify capability gaps, then works with content, grounding, and engineering partners to specify and test fixes. The analyst will build evidence-backed business cases, follow recommendations through delivery, measure outcomes, and communicate findings to technical and non-technical stakeholders. The position works closely with Data, Product, Quality, and CX on the team responsible for FBG's AI customer experience.

Requirements

At least 2 years of experience in business analytics, data analysis, business operations, customer experience analytics, or a closely related role.
Strong SQL skills, including independently pulling, joining, and validating data across large datasets.
Experience owning recurring metric reporting and analysis and being accountable for its quality.
Experience building BI views and dashboards used by others to make decisions; examples include Tableau, Looker, Power BI, or Sigma.
Ability to identify specific root causes, estimate their value, and recommend fixes based on changes in metrics.
Comfort analyzing unstructured conversation data at volume, recognizing patterns, and validating them quantitatively.
Experience writing findings and recommendations for partners outside the reporting line and seeing some recommendations acted upon.
Conceptual understanding of LLM-powered systems, including prompting, retrieval-augmented generation (RAG), knowledge grounding, and evaluation, sufficient to distinguish grounding problems from prompt problems and tool gaps.
Clear written and verbal communication, including the ability to produce an understandable one-page summary.
High intellectual curiosity and an AI-first approach to work.

Nice To Haves

Experience analyzing production conversational data from chatbots, voice systems, or agent-assist tools.
Experience with Snowflake, Databricks, or a comparable cloud data platform, and dbt or similar transformation tooling.
Python or R skills for analysis beyond what SQL handles cleanly.
Background in customer experience, contact center, or support operations analytics, particularly analysis of 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 the effect of content quality on AI answer quality.

Responsibilities

Maintain and report customer-facing AI agent metrics, including containment, resolution, deflection, escalation rate, repeat contact, and customer satisfaction at case and subtype levels.
Help establish success measures and instrumentation for each new AI surface as it launches.
Build and maintain BI views and dashboards used by the team and Product partners, keeping them accurate as products, policies, and promotions change.
Write data requirements, including event definitions, tagging, table structures, and data quality checks, and partner with Data to implement them.
Identify and flag when reported metrics or underlying data are no longer trustworthy.
Conduct case type and subtype deep dives to identify customer stall points in AI interactions, such as repeated questions, loops, silence, abandonment, or requests for a human.
Quantify stall points by frequency, affected subtypes and markets, and impact on escalations, repeat contacts, and resolution.
Combine conversation data, interaction quality scores, customer and associate feedback, behavioral data, and operational metrics to identify drivers of performance.
Distinguish actionable issues from noise and communicate that distinction candidly.
Compare live-agent and AI handling of the same contact types to identify agent actions, lookups, and tools the AI cannot yet use.
Document AI capability gaps, what would close each gap, and the potential containment or resolution improvement.
Analyze escalation boundaries and assess whether handoffs were necessary.
Identify root causes and route them to the appropriate owner and lever, such as grounding and knowledge content, prompt design, tone, tool access, handoff timing, or policy ambiguity.
Specify recommended changes to grounding, tone, or instructions and work with content, grounding, and engineering partners to make findings testable and actionable.
Conduct recurring performance reviews for owned case types and maintain a ranked, evidence-backed opportunity list.
Develop evidence-backed narratives from conversations and queries, including patterns, representative examples, problem scale, and specific requests.
Quantify proposed-fix impacts, including contacts avoided, resolution lift, handle time, cost, and customer experience, with explainable assumptions.
Prepare business cases and recommendations for Product partners and bring them into intake with the manager.
Track recommendations through delivery and measure whether the changes achieve expected results.
Report what shipped and what changed, including outcomes that did not meet expectations, to inform future prioritization.
Partner with Data, Product, Quality, and CX and communicate analysis clearly to technical and non-technical audiences.
Write findings and recommendations and contribute to leadership-facing materials.
Document and share findings so the wider team can use them.