AI-Powered Growth Intelligence Associate

Groupon • Chicago, IL

8h • $90,000 - $120,000 • Hybrid

About The Position

Groupon is a marketplace connecting customers with new experiences and services and helping local businesses thrive, having worked with over a million merchant partners worldwide and connecting over 16 million customers. The company is committed to helping local businesses succeed on a performance basis and is undergoing a transformation with a relentless pursuit of results. Despite its global scale, Groupon maintains a culture that inspires innovation, rewards risk-taking, and celebrates success, offering significant autonomy and impact to individuals. This role is central to Groupon's initiative to rebuild its sales strategy using AI, specifically through 'Project Foundry,' a fleet of AI agents designed to enhance the sales force. The AI-Powered Growth Intelligence Associate, reporting directly to the CSO, will be responsible for building and owning the intelligence layer that fuels Foundry. This includes managing Groupon’s lead scoring architecture, routing logic, and pipeline forecasting. The position involves extracting signals from a data-rich commercial environment, including proprietary consumer transaction signals, merchant health data, and conversion history, to build models and use AI to transform raw data into actionable queues for sales representatives. The intelligence layer developed by this role will drive the autonomous agents of Foundry, influencing lead sequencing, merchant reactivation, and inbound inquiry routing, as well as the ranked order of leads in a rep's queue. The ultimate goal is to transform Groupon’s raw lead data into a predictive intelligence system, ensuring every rep accesses the right account, in the correct order, with the appropriate context daily.

Requirements

• A degree from an Ivy League or equivalent top-tier university — data science, computer science, economics, statistics, or a quantitative field that trained you to think in systems.
• Up to 2 years of professional experience.
• Hands-on fluency with Python and data tools — you have built models and connected data sources. You need to build a working pipeline without asking someone else to write the code.
• Active use of AI tools — you have used LLM APIs (OpenAI, Anthropic, or equivalent) to build something real, and you know the difference between a demo and a deployed workflow.
• Analytical rigour without a safety net — you build your own measurement frameworks. You do not accept a metric you cannot define or a result you cannot explain.
• Commercial curiosity about how leads and sales motions work — you don’t need a sales background, but you need the instinct to connect what you build to a GP outcome.

Nice To Haves

• Builder first. Your instinct is to build before you ask, and to measure before you conclude.
• AI-native, not AI-curious. You use AI the way a previous generation used spreadsheets: constantly, purposefully, and with measurable output.
• Comfortable owning something fragile. You stabilise it, document it, then improve it.
• Rigorous with ambiguity. When there is no clean data, you make a reasoned call, document your assumptions, and build a way to test whether you were right.
• Commercially wired. You connect what you build to pipeline outcomes. If conversion doesn’t move, the model isn’t done.

Responsibilities

• Own the lead scoring model — Design, build, and continuously refine the AI-powered scoring model that determines which leads get prioritised. Use firmographic data, behavioural signals, merchant health indicators, and conversion history to predict outcomes — not just activity.
• Define and improve lead routing — Translate scoring intelligence into assignment rules that get the right lead to the right rep at the right time. Work with the Sales Operations team on implementation; you define the logic and own the outcome.
• Build pipeline intelligence — Give the CSO and Sales leadership a live view of where pipeline is healthy, where it is at risk, and what is likely to convert this quarter.
• Run experiments on live data — Test whether a new signal improves conversion. Measure the delta. Iterate. Every change has a before/after record. You do not ship and move on.
• Mine Groupon’s data advantage — Use CRM data, call transcripts, and enrichment sources to find signals no competitor can replicate. This is Groupon’s moat. You are building on top of it.

Benefits

• Base salary: 90k-120k base + 10% ABP Bonus
• Location: Downtown Chicago (hybrid, 3 days a week in-office)
• Alternate locations: Can be remote for the right fit
• Benefits start the 1st of the month after your start date — Medical, Dental, Vision, Life Insurance, Disability, FSAs, EAP, 401(k) match, ESPP, flexible PTO, and more
• Employee Resource Groups & inclusive team culture

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level