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Magic Eden is a crypto marketplace that reached unicorn status in 9 months. The company is now launching Dicey, a crypto casino and sportsbook platform targeting the $180B+ iGaming and sports betting markets. You'll join a small, fully remote team that ships fast and is rebuilding how growth teams operate around AI from the ground up.
In this role, you own retention and monetization for the post-signup player journey. The iGaming industry largely competes on acquisition; almost nobody has figured out personalized retention at scale. You'll be the person who changes that.
Key responsibilities:
- Own the full retention and monetization growth loop: set targets, identify leverage points, and drive P&L outcomes for everything after player signup
- Expand and own the player segmentation framework, deciding how it evolves, what signals feed it, and how the company uses it
- Build the AI-native growth stack: analysis, agents, and automated pipelines that surface segments, generate offer candidates, and run campaigns with minimal manual work
- Design and ship targeted offers for specific segments and moments (reload bonuses, lossback, wager bounties, challenges, win-back sequences); size, spec, ship, and measure them
- Spend time in the data warehouse and on-chain data finding patterns and turning them into campaigns
- Expand player signals through on-chain activity, status-match programs, social account linking, and other sources
- Run campaigns across email, in-app, Telegram, and other channels, with AI handling the heavy lifting
- Partner with engineering and design to ship product surfaces that make campaigns repeatable
You'll be hands-on: writing queries, running analyses, sending campaigns, and writing specs yourself rather than just briefing others. Half the job is identifying the right problem; the other half is running experiments to find the answer.
Requirements:
- Strong intuition for player psychology and incentive design. Background in gambling, gaming, fintech, or consumer marketplaces is helpful; genuine curiosity about human behavior is required
- Deeply analytical: able to move from a vague question to a clean cohort cut without waiting on a data scientist
- AI-native by default: already use AI to write queries, build analyses, and automate work; want to push much further
- High output with good judgment: generate many ideas and know which ones are worth testing first
- Bias to do it yourself: comfortable sending emails, running queries, and writing specs
- Comfortable with ambiguity and building playbooks from scratch
Ideal backgrounds include: Growth PM at a data-heavy consumer company, MBB consultant transitioning to product ownership, or product-minded data scientist/analyst who wants to ship rather than just report.
