Senior Business Analyst, Operations Automation

Fanatics Betting & Gaming•Denver, CO

19h

About The Position

Fanatics Betting & Gaming is hiring a Senior Business Analyst, Operations Automation to analyze customer experience in AI agent conversations, with a focus on containment and resolution at the case and subtype level. The analyst will examine real customer conversations, identify where customers get stuck, compare AI handling with live-agent resolution, and determine whether issues stem from knowledge grounding, prompts, tone, tool access, handoff timing, or policy ambiguity. They will quantify the impact of problems and proposed fixes, build evidence-backed business cases, and bring recommendations into Product intake with their manager. The role owns analysis from day one and sits on the team responsible for FBG’s AI customer experience. It works week to week with Data, Quality, Product, and CX to establish measurement for new AI surfaces, maintain trusted performance reporting, specify data requirements, and track whether shipped changes achieve their intended outcomes. The position also involves communicating findings to technical and non-technical partners and contributing to leadership-facing materials. Fanatics Betting and Gaming is the online and retail sports betting subsidiary of Fanatics. Its sportsbook is available to 95% of the addressable online sports bettor market in the U.S.; its online casino operates in Michigan, New Jersey, Pennsylvania, and West Virginia. The business operates 22 retail sports betting locations and is headquartered in New York, with offices in Denver, Leeds, and Dublin.

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 the ability to independently pull, join, and validate data across large datasets.
Experience owning recurring reporting and analysis for a metric and being accountable for its quality.
Experience building BI views and dashboards used by others to make decisions, using Tableau, Looker, Power BI, Sigma, or similar tools.
Ability to investigate metric changes, identify specific root causes, estimate their value, and recommend fixes; diagnostic skill is emphasized over modeling sophistication.
Comfort working directly with unstructured conversation data, reading transcripts at volume, identifying patterns, and validating them quantitatively.
Experience writing findings and recommendations for cross-functional partners 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 and tool gaps.
Clear written and verbal communication, including the ability to write a concise one-pager that is readily understood.
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 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 key customer-facing AI agent metrics, including containment, resolution, deflection, escalation rate, repeat contact, and customer satisfaction, at case and subtype levels.
Help establish measurement for each new AI surface, including defining success and ensuring instrumentation is available before launch.
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 when data or metrics are no longer trustworthy and flag the issue early.
Conduct deep dives into case types and subtypes to identify points where customers rephrase, loop, abandon, go silent, or request a human, and investigate what preceded those outcomes.
Quantify customer stall points by frequency, affected subtypes and markets, escalations, repeat contacts, and lost resolution opportunities.
Combine conversation data, interaction quality scores, customer and associate feedback, behavioral data, and operational metrics to identify root causes; distinguish meaningful issues from noise.
Compare AI handling with live-agent handling for the same contact types and identify agent lookups, tools, or actions the AI cannot yet perform.
Document specific AI capability gaps and estimate their potential containment or resolution impact.
Determine whether escalations were necessary and identify appropriate fixes, such as changes to grounding, knowledge content, prompts, tone, tool access, handoff timing, or policy clarity.
Work with content, grounding, and engineering partners to specify findings clearly enough to build and test.
Run recurring performance reviews for owned case types and maintain a ranked, evidence-backed list of improvement opportunities.
Develop clear narratives from conversations and queries, including patterns, representative examples, problem scale, and specific recommendations.
Estimate the impact of proposed fixes, including contacts avoided, resolution lift, handle time, cost, and customer experience, and explain the assumptions used.
Prepare business cases and recommendations that Product partners can act on, and bring them into intake with the manager.
Track recommendations through delivery and measure whether shipped changes produce the expected results; report what shipped and what changed.
Collaborate with Data, Product, Quality, and CX, and communicate analysis clearly to technical and non-technical audiences.
Write up findings and recommendations, contribute to leadership-facing materials, and document learnings for team use.