Senior Analyst, Operations Automation

Fanatics Betting & Gaming•Denver, CO

1d•$86,000 - $115,000

About The Position

Fanatics Betting & Gaming (FBG) is seeking a Senior Analyst, Operations Automation to analyze customer experiences within its AI agent conversations. The role focuses on containment and resolution performance, examining customer interactions at the case-type and subtype level to uncover recurring issues, identify where customers stall, and determine whether problems stem from knowledge content, prompts, tone, tool access, handoff timing, or policy ambiguity. The analyst will own recurring metrics and reporting, build BI views and dashboards, help establish measurement for new AI surfaces, and define data requirements in partnership with Data. They will compare AI handling with live-agent resolutions, quantify potential improvements, and turn findings into evidence-backed business cases and recommendations for Product partners. The analyst will follow recommendations through implementation and measure their outcomes. The role works closely with Data, Quality, Product, and CX, translating analysis into clear materials for technical and non-technical audiences. It offers ownership of analysis from day one and the opportunity to develop expertise in 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 data quality.
Experience building BI views and dashboards used by others to make decisions, using Tableau, Looker, Power BI, Sigma, or similar tools.
Ability to identify root causes behind metric changes, estimate their value, and recommend fixes; diagnostic ability is emphasized over modeling sophistication.
Comfort analyzing unstructured conversation data, including reading transcripts at volume, identifying patterns, and validating them quantitatively.
Experience presenting findings and recommendations to partners outside the reporting line and having 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 or tool gaps.
Clear written and verbal communication, including the ability to produce concise, understandable written analyses.
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.
Familiarity with Snowflake, Databricks, or comparable cloud data platforms, 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 involving 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 affects AI answer quality.

Responsibilities

Maintain and report customer-facing AI agent metrics, including containment, resolution, deflection, escalation rate, repeat contact, and customer satisfaction at the case and subtype level.
Help establish measurement and instrumentation for each new AI surface before it launches.
Build and maintain BI views and dashboards for 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.
Flag when metrics or underlying data are no longer trustworthy.
Conduct case-type and subtype deep dives to identify points where customers rephrase questions, loop, go silent, abandon, or request a human, and analyze what preceded those events.
Quantify and rank customer stall points by frequency, concentration across 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 meaningful issues from noise and clearly communicate the difference.
Compare how live agents and the AI agent handle the same contact types, identifying agent lookups, tools, and actions that the AI cannot yet perform.
Document specific AI capability gaps and the containment or resolution gains that addressing them could deliver.
Assess whether AI escalations to human agents were necessary.
Identify root causes and route issues to the appropriate owners and solutions, such as knowledge content, grounding, prompts, tone, tool access, handoff timing, or policy clarification.
Specify changes to grounding, tone, or instructions and work with content, grounding, and engineering partners to prepare findings for implementation and testing.
Run recurring performance reviews for owned case types and maintain a ranked, evidence-backed list of improvement opportunities.
Create evidence-backed narratives from conversations and queries, including patterns, representative examples, problem size, and specific recommendations.
Quantify proposed fixes in terms of contacts avoided, resolution improvement, 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 they achieve the expected outcomes.
Report what shipped and what changed, including outcomes that did not match expectations.
Partner with Data, Product, Quality, and CX and communicate analysis clearly to technical and non-technical audiences.
Write findings and recommendations, contribute to leadership-facing materials, and document learnings for team use.