AI Content Strategist, Research

Responsibilities

• Report on what actually works when people shop online, using checkout data nobody else has.
• The System You'll Need to Model - Data-driven public relations as a supply chain. Proprietary first-party data becomes a citable study; the study rides two distribution lanes, proactive pitches and reactive reporter queries, into press coverage and AI answers. Every serious research-communications shop runs this loop. Ours runs on data nobody else has, and the study is the unit of leverage.
• Two citation markets with different physics. Journalists are pitch-driven and relationship-gated; AI answer engines are retrieval-driven and quotability-gated. Being named and being cited are separate outcomes in both, and a study that wins one can lose the other.
• Agents as research staff, and verification as the craft that makes them usable. Warehouse pulls, research sweeps, and first drafts run through agents here; the job is proving what they hand back. What a golden check looks like for a research claim, which tests catch a silent join fanout or a survivorship artifact, when you re-pull a number by hand.
• Cortex, the newsroom you publish inside: the shared AI brain that runs the company and the product family we sell, it answers its own questions from more than 8,600 documents. A newsroom data desk like this ran three people in 2023; the agent tooling is why it is one seat here. The company moves weekly, nobody hands you a brief, and reading where the system is going is part of the job.
• The canonical stat surfaces. The numbers the press and answer engines quote, kept fresh, sourced, and honest. When a study calls for social copy, you supply the data moment; our brand and content team owns that channel.
• The seat, chartered. You work directly with Dakota Nunley, our Head of Search Product, who runs the research program and its beat. Within your first quarter you co-sign a seat charter anchored to one number: published studies that clear a stated method bar at this cadence, plus a clear split between what you publish directly and what your lead reads first.

Requirements

• You reason about numbers the way a good editor reasons about sources: what would make this wrong, what is the honest denominator, and what time window the claim covers.
• You feel the difference between a finding and a query artifact, update fast when your model is wrong, and write clearly because the written study is the meeting.
• You go to the data first. Agents run warehouse queries, research sweeps, and verification passes under your direction, and because you can do every step yourself — the pull, the sanity checks, the draft, the pitch note — you know when to trust or reject what an agent hands back.
• You have published data stories that stand on numbers you pulled yourself, whether on a newsroom data desk, in a data journalism program, on a research team, or on a beat you ran with a scraper and a spreadsheet. We judge the artifact and the reasoning wherever you did the work, with no degree required.

Benefits

• 100% company-paid family medical, dental, and vision premiums and an uncapped AI tooling budget steered by return.
• Profit Interest Unit (PIU) ownership and profit-share program ($0 strike price units sharing in annual free-cash-flow distributions and tender liquidity, earned on performance milestones after joining, never day-one vesting)