JD Case Study · Target

Director, Quantitative UX Research & AI Enablement

Where: Minneapolis, MN · remote-eligible
Pays: $168K–$303K
Posting: Read the job description →
Shelf: All briefings

A Director owning quantitative UX research, experience measurement, and Voice-of-Customer — translated into product decisions — plus responsible AI-enablement of the research function. The VoC + measurement + experimentation muscle here is exactly the one I built for a CX Research & Market Intelligence brief; and the velocity of this portfolio is itself proof of AI-scaled research workflows.

One kind of number here. The analysis below runs on illustrative figures invented to show method. No Target data is used or implied; the operating logic is the deliverable.

Jun 22, 2026

What they’re actually buying

Make Target learn faster about its guests

Make Target learn faster about its guests. Own UX metrics, benchmarking, survey research, longitudinal VoC, and experimentation — and turn them into decisions product acts on. Then make that research AI-ready: structured, reusable, discoverable, with responsible guardrails for agents and synthetic users. The job is rigor and reuse, at scale — pairing what guests do with what they say.

The first 90 days

Map the measurement, standardize it, prove AI on it

The order matters. Nobody should change a system they haven’t reconciled, and nobody should present a verdict before the numbers under it can be trusted.

Days 1–30

Map the measurement landscape

Audit the UX metrics, VoC programs, survey and benchmarking work. Baseline the experience KPIs, meet UX / Product / Analytics, and find the highest-value measurement gaps and the decisions they’d unblock.

Days 31–60

Standardize & make reusable

Establish survey-design / sampling / analysis standards and a reusable insights repository, and ship an experience scorecard tied to product roadmaps. Pilot AI-assisted synthesis (open-end coding) behind a quality bar.

Days 61–90

Prove AI-enabled research

Ship one responsible AI workflow — AI-assisted coding or a guard-railed synthetic-user pilot — with a governance and quality framework, plus a benchmark study that lands a real roadmap decision.

Signature analysis · illustrative data

Signature analysis: behavior vs. voice

The quant-research core: pair the behavioral funnel with the VoC signal and look for the gap — the step where guests succeed behaviorally but say it hurt (or quit but rate it fine). That divergence is the fundable research question. I’ve done exactly this: on my Upbound brief, a 1.2-star Apple-vs-Google-Play gap on lease-to-own apps flagged an Android friction no behavioral metric surfaced.

Where do guests behave differently from what they say?Indexed to the first stage

Sessions100indexed to 100

Successful search72

Engaged (PDP / add-to-cart)44

Completed task / checkout28

Reported satisfied (VoC)24where behavior and voice agree

Illustrative figures to demonstrate the lens — behavioral funnel against VoC, with AI (synthetic users, AI-assisted open-end coding) as the way to scale the measurement responsibly.

Requirement → proof

The posting, answered line by line

Each line of the posting against something already built and running on this site. Where the fit is a ramp rather than a match, the row says so.

What Target asks for	What I’ve already shipped
Quantitative research & experience measurement	A real VoC quant finding — the Apple-vs-Google-Play rating divergence as a fundable research signal.
Voice-of-Customer / longitudinal listening → action	VoC synthesized into a theme → opportunity → action read for executives.
Experimentation & behavioral analysis	An A/B + incrementality readout with confidence intervals and a sample-size calculator.
Research standards, governance & reusable repository	Every artifact here follows one sourcing standard — cited, dated, reusable. That’s research-ops discipline, shown.
AI-enabled, scalable research workflows	The velocity of this portfolio — a dozen sourced, cited analyses — reflects AI-accelerated research and synthesis workflows I designed and direct.

The same engine

This role is the CX-research and experience-measurement muscle I already put on display in my Upbound market-intelligence brief — VoC signals, experimentation, insight-to-decision — now scaled with AI. Same engine, research side. I don’t treat "quant UX research" and "market intelligence" as different jobs; they’re the same rigor pointed at a different question.

See the CX-research brief →

Hire the work, not the headcount

You’ve already priced this role. Here’s what the money buys.

Target says Director, Quantitative UX Research & AI Enablement is worth $168K–$303K a year (posted base range). Fully loaded — payroll taxes, health insurance, benefits, roughly +25% — the midpoint runs about $295K a year: $142 an hour, for all 2,080 hours, ramp and meetings included. And the ramp is real: a new leader’s breakeven averages about 6 months (Watkins, The First 90 Days) — call it $148K spent before the first shipped thing. The candidate who interviews well will be great in the room and diligent on the kanban board. What they won’t do is build the systems. That’s the part I sell.

The build · 90 days

$30,000

~120 hours at $250/hr ships the first-year plan on this page as working systems on your data — dashboards, pipelines, automations that keep running after I stop billing. You buy the 120 hours that ship, not the 2,080 you’d pay a hire for. Value in week one, not month 9; compare $148K for the hire’s ramp alone.

The bench · monthly

$3,000/mo · 10 hrs

10 hours of me a month, plus my agents running your data refreshes on a schedule — the same fleet that keeps this site current — evals green, costs flat, and me on call. Month to month; cancel anytime; the systems are yours either way.

Prepay the quarter and it’s $7,500 for 3 months — $1,500 off the $9,000 month-to-month price, for paying up front.

Founding-build rate: the first 3 builds run $15,000 — half the list price — in exchange for a named, written case study — open now. Email me to take one; when the third case study ships, so does this rate.

Or keep the req. Hire the person who’s great in the room — and pay me to make them dangerous: the same $30,000 build, handed to your new director on day one, pulls their breakeven from month 6+ to month two. The expensive mistake isn’t the hire; it’s the hire with no system underneath them. This case study is the free sample of what the system looks like.

Email me the req Work with me

Quantitative research that pairs behavior with voice, translates to decisions, and scales with AI — I’ve already shipped it, in public. Let’s talk about how Target learns. — Paul Brown