Senior Analyst, Operations Automation Denver, CO, United States

Fanatics Inc.; Denver (CO); On-site; USD 90,000 - 120,000; Full time

Job summary

Fanatics Betting & Gaming is hiring a Senior Analyst, Operations Automation to dive into the customer experience inside our AI agent conversations. You will define containment and resolution metrics, identify stall points, and build the business case for fixes with cross-functional partners.

You’ll own analytic work from day one, partnering with Data, Quality, Product and CX to measure AI performance, frame recommendations, and track impact as new AI surfaces launch.

Qualifications

• Experience in analytics for AI or CX is advantageous.
• Strong problem-solving and storytelling with data.

Responsibilities

• Maintain and report AI agent metrics: containment, resolution, deflection, escalation, and CSAT at case/subtype levels.
• Define success metrics for new AI surfaces and instrument them before launch.
• Build BI views and dashboards for AI performance used by Product and CX teams.
• Write data requirements: event definitions, tagging, and table structures with Data.
• Flag early when data is not trustworthy and document issues.

Skills

Data analysis

SQL

BI dashboards

Tools

Python

Excel

Job description

Fanatics is building a leading global digital sports platform. We ignite the passions of global sports fans and maximize the presence and reach for our hundreds of sports partners globally by offering products and services across Fanatics Commerce, Fanatics Collectibles, and Fanatics Betting & Gaming, allowing sports fans to Buy, Collect, and Bet. Through the Fanatics platform, sports fans can buy licensed fan gear, jerseys, lifestyle and streetwear products, headwear, and hardgoods; collect physical and digital trading cards, sports memorabilia, and other digital assets; and bet as the company builds its Sportsbook and iGaming platform. Fanatics has an established database of over 100 million global sports fans; a global partner network with approximately 900 sports properties, including major national and international professional sports leagues, players associations, teams, colleges, college conferences and retail partners, 2,500 athletes and celebrities, and 200 exclusive athletes; and over 2,000 retail locations, including its Lids retail stores. Our more than 22,000 employees are committed to relentlessly enhancing the fan experience and delighting sports fans globally.

About the Team

Launched in 2021, Fanatics Betting and Gaming is the online and retail sports betting subsidiary of Fanatics, a global digital sports platform. The Fanatics Sportsbook is available to 95% of the addressable online sports bettor market in the U.S. Fanatics Casino is currently available online in Michigan, New Jersey, Pennsylvania and West Virginia. Fanatics Betting and Gaming operates twenty-two retail sports betting locations, including the only sportsbook inside an NFL stadium at Northwest Stadium. Fanatics Betting and Gaming is headquartered in New York with offices in Denver, Leeds and Dublin.

Overview:
Fanatics Betting & Gaming (FBG) is hiring a Senior Analyst, Operations Automation to dig into the customer experience inside our AI agent conversations.
Containment and resolution are the numbers you'll live in, and you'll work them at a level of detail nobody else here has time for: one case type, then one subtype, then the next one. In practice that means picking up a subtype with your manager, reading through a stack of real conversations, and working out what's actually going wrong. Sometimes customers get stuck in the same spot over and over. Sometimes a live agent clears the same contact in two minutes because they can check something the AI can't reach yet. Sometimes the AI is doing roughly the right thing badly: the tone is off, the knowledge article behind it is thin, or the prompt is pushing it down the wrong path.
Finding the problem is half the job. The other half is making the case for the fix — what it's costing us now, what the fix is worth, and a story clear enough that a Product partner can act on it. You'll build that case and bring it into intake alongside your manager. We would rather hire someone solid at both halves than someone brilliant at one.
This is a role where you learn the craft of AI customer experience with real ownership of the analysis from day one. You'll sit on the team that owns FBG's AI customer experience and work with Data, Quality, Product, and CX week to week.
Responsibilities:

• Maintain and report the key metrics for our customer-facing AI agent — containment, resolution, deflection, escalation rate, repeat contact, and customer satisfaction at a case and subtype level.
• Be part of the team that stands up measurement for each new AI surface as it launches, so no surface goes live without a definition of success and the instrumentation to track it.
• Build and maintain the BI views and dashboards your team and Product partners use to read AI performance, and keep them accurate as products, policies, and promotions change.
• Write the data requirements — event definitions, tagging, table structures, and data quality checks — the metrics depend on, and partner with Data to get them built.
• Flag it early when a number stops being trustworthy. Knowing when your own data is wrong matters as much as reporting it.

Find where customers get stuck

• Run deep dives at the case type and subtype level to find the stall points inside the AI interaction — where customers rephrase the same question, loop, go silent, abandon, or ask for a human, and what preceded it.
• Quantify each stall point so it can be ranked: how often it happens, which subtypes and markets it concentrates in, and what it costs in escalations, repeat contacts, and resolution loss.
• Pull together signals from more than one place — conversation data, interaction quality scores, customer and associate feedback, behavioral data, and operational metrics — into one view of what's actually driving the number.
• Distinguish issues worth fixing from noise, and be candid about which is which.

Compare the AI to how live agents handle it

• Compare how live agents resolve a contact type to how the AI agent handles the same contacts, and identify what agents are doing that the AI cannot do yet — the lookups they run, the tools they touch, the actions they take.
• Write up each gap as a clear, specific finding: what the AI would need to close it, and the containment or resolution upside if it did.
• Pay attention to the escalation boundary. Where the AI hands off, look at whether it had to.

Point to the right fix

• Sort root causes well enough to route them to the right owner and the right lever — grounding and knowledge content, prompt and instruction design, tone, tool access, handoff timing, or underlying policy ambiguity.
• Where the AI is already doing the right thing badly, describe the change specifically: what the grounding should say, where the tone misses, which instruction is producing the wrong behavior.
• Work with content, grounding, and engineering partners to get your finding specified tightly enough to be built and tested.
• Run recurring performance reviews on the case types you own, and keep a ranked, evidence-backed list of the opportunities in them.

Build the story

• Turn a pile of conversations and queries into a clear, evidence-backed narrative — the pattern, the representative examples, the size of the problem, and the specific ask.
• Quantify the impact of a proposed fix — contacts avoided, resolution lift, handle time, cost, customer experience — with assumptions you can walk someone through.
• Package findings into business cases and recommendations a Product partner can act on without a translation layer,