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Key statistics

The identity block — every fact is canon, none is generated.

1 of 8

Track

Management

Family rungs priced

M1 · M2 · M3 · M4 · M5 · M6

41-0000

retail_hospitality

Coordinates

3 / 4 spaces

pay@2 · 2026-07

What this role is

Supervises frontline associates and daily operations within a dedicated product department in a retail store. At the Manager (Team Lead) (M1) level, the role owns a single team, day-to-day delivery and people issues, and drives team output and health. Direct people management of one team.

What this level does

Supervises the daily product operations of a single store unit — leading inventory shifts, overseeing all back-of-house product operations, and ensuring both the floor and BOH are stocked and well-organized each day.
Ensures the store is 'ready for business' daily, handling sales transactions and enforcing compliance with relevant legislation and company product-handling standards.
Directs a small team of retail/product specialists in product setup and cataloging in the POS, maintaining accurate product records and an organized, visually appealing retail space.
Runs on-shift product-knowledge training sessions so staff can perform engaging demonstrations and answer product specification questions.

Knowledge applied: Functional expert in store product operations — POS product setup, inventory handling, and product demonstration — applying established practices to run a single unit's daily product operations.
Complexity & problem solving: Limited scope; resolves routine product-readiness, stock-accuracy, and floor-organization issues using established procedures.
Collaboration & interaction: Daily interactions with store staff and peer supervisors to keep the store ready for business.
Typical experience: Functional product/retail expert with some leadership exposure; first-line supervision of individual contributors.

Skills at this level

POS Product Setup & Cataloging: — Uses point-of-sale (POS) systems to build products, catalog SKUs, and maintain accurate in-store product records so items are correctly configured for sale and demonstration.
Back-of-House Inventory Readiness: — Leads inventory shifts and oversees BOH product operations to ensure the floor and back-of-house are stocked, well-organized, and 'ready for business' each day.
Product Demonstration & Knowledge: — Memorizes product specifications and performs engaging in-store demonstrations to customers and stakeholders, and trains floor staff to do the same.
Product Sales Training & Coaching: — Coaches store staff in selling skills and product knowledge and conducts product and sales training for the floor and marketing teams.
Store Product Performance Analysis: — Monitors store-level product performance metrics and interprets trends to inform in-store positioning, replenishment, and promotion tactics.
Customer Feedback Loop to Product Teams: — Consolidates customer and floor-staff feedback on products and relays structured recommendations to product development and marketing teams.
Launch & Promotion Coordination: — Manages cross-functional launch and promotion projects in-store, aligning POS cataloging, staff training, and BOH readiness ahead of go-live.
Retail Compliance & Legislation: — Ensures store product handling and sales transactions comply with relevant legislation and company product-handling standards.
Shrink & Inventory Risk Management: — Manages shrink, compliance, and stock exposure risk across the managed store product footprint through inventory controls and audits.
Product-Operations Standards Setting: — Defines and enforces the methods and standards by which stores execute product setup, cataloging, demonstration, and inventory readiness at scale.

Model-authored from 23 retrieved sources, then adversarially reviewed by an independent judge panel and held to a minimum-evidence floor before publication. Distinct from corpus-extracted content, which is labelled as such.

Pay across the ladder

Product Specialty — median with , by . Real canon data.

National-base pricing — this family's function differential is pending review, so dollar levels are not function-adjusted (level-over-level % is unaffected).

Pay figures are modeled estimates from our market equation — level-differentiated, with honest-wide bands. They refresh as new leveling evidence lands; treat per-role dollars as directional.

Demand — is the field growing, or just turning over?

SOC 41-0000 · national

Employment growth 2019–2024, BLS OEWSobserved

-5.0%First-Line Supervisors of Retail Sales Workers · United States

1,171,900 → 1,113,160 jobs

125,100 openings / year, split into:

replacement / exits 132,263

Growth — new jobs (-6%)Exits — backfill (100%)

This occupation is projected to contract — every opening is replacing someone leaving, not net new demand.

Projected growth 2024–34 contracting: -5.0%
Growth share of hiring: -6%
Exit / retention pressure high: 100%
Openings / year: 125,100

Two quantities, kept apart: observed = employment growth differenced from two BLS OEWS vintages (what already happened); projected = BLS Employment Projections 2024–34, openings decomposed into growth + exits (growth derived from projected employment change). Neither is a pay figure.

Where this role sits — growth vs. pay

level M1 held

Demand axis is projected growth (not raw openings — that just measures churn). A curated, de-duplicated set of recognizable occupations held at this level; this role is highlighted.

Where it pays — by metro

OEWS metro median

San Jose-Sunnyvale-Santa Clara, CA$139k

San Francisco-Oakland-Fremont, CA$125k

Washington-Arlington-Alexandria, DC-VA-MD-WV$115k

Boulder, CO$114k

Seattle-Tacoma-Bellevue, WA$114k

Boston-Cambridge-Newton, MA-NH$109k

Lexington Park, MD$106k

Trenton-Princeton, NJ$106k

Pay only — metro-level demand isn't in public data (it needs job postings). National median $84k; the demand-cube initiative will add growth-by-metro.

What changes across the ladder

Level-distinction canon isn't available for the adjacent levels of this profile yet.

Level differentiators

What separates M1 (M1 — Manager (Team Lead)) from the levels around it — this level only. Every word is real canon for this profile.

Scope: A single team
Autonomy: Manages within established goals
Complexity: Day-to-day delivery and people issues
Impact: Team output and health
Decision rights: Owns team execution, hiring input, performance
Leadership: Direct people management of one team

Full level-by-level matrix (side by side across the ladder) lives on the Ladder drill-down — not here.

Promotion

The next rung from M1 is M2.

Performance criteria for this profile render in the assessment kit →

People also watch

Nearest coordinate neighbors — same , same level. Each row's (confident / review / no match) comes from its — computed, not curated.

Profile	Key	Median	Δ structural
VP, Product Specialty Merchandising	jf_product_specialty::E1	$169,459†	confident0.06
Product Sales Associate	jf_product_specialty::P1	$61,862†	confident0.06
Retail Sales Associate	jf_product_specialty::S1	$37,323†	confident0.06
Senior Department Supervisor, Product Specialty	jf_product_specialty::M2	$110,455†	confident0.06

†National-base pricing — this family's function differential is pending review, so the dollar level is not function-adjusted.

Capability cluster

O*NET 41-1011

Core to this role. Click any element with an ⓘ for its O*NET definition. A job-specific capability model (SkillFrame) will sharpen this and add worked examples.

The Job Swatch

M1

jf_product_specialty::M1

Department Sales Supervisor

Product Specialty

The swatch is JobFrame's core primitive: a hueset deterministically by the role's superfunction, the profileKey as its color code, the level ladder, and a content fingerprint. Two swatches that look alike are alike.

Because every role carries computable coordinates, similarity, leveling, and pricing become measurable instead of arguable— the whole point of "Pantone for jobs."

Structural

structural@1

Where it sits— position in the work taxonomy + level (weighted Gower, level > function > focus).

Track: M
Superfunction: retail_hospitality
Function: store_operations
Family: product_specialty
Universal level: 1 / 8

Content

content@2

What it's about— a fingerprint over the role's tasks & capabilities.

Two roles can sit far apart structurally yet be close in content — that gap is itself a signal.

3-state matching

How any two swatches compare — a real distance in coordinate space, resolved to a calibrated verdict (never a fake "94% match").

Cashier · M1review0.17

Loss Prevention · M1review0.17

Optical · M1review0.17

Pharmacy · M1review0.17

Store Management · M1review0.17

General · M1review0.17

Editions — frozen & versioned

structural@1live

Released 2026-06-17

content@2live

Released 2026-07-28

pay@2live

Released 2026-07-02

semantic@1live

Released 2026-07-02

Coordinates belong to a frozen, versioned edition — a coordinate computed today still means the same thing next quarter. Re-fitting issues a new edition, so comparisons are never silently redefined.

How to read this profile

Every number here is traceable — to a named source, a stated method, and an as-of date — and each value is labeled by how we know it: observed from data, computed by a model, projected forward, or governed canon. Nothing is a black box, and nothing is invented to fill a gap.

Where every number comes from

What	Source & method	Edition	State
Median pay + range	JobFrame pay equation (function base × family multiplier × level × focus differential)	pay@2	computed
Pay across the ladder	Same equation applied at each universal level	pay@2	computed
Demand — growth & openings	BLS Employment Projections; openings decomposed into growth + exits	EP 2024–34	projected
Pay by metro	BLS OEWS metropolitan medians	OEWS 2024	observed
Level distinctions	JobFrame universal-level canon (scope · autonomy · complexity · impact · decision rights · leadership)	canon	canon
Capability cluster	O*NET Knowledge / Skills / Abilities	O*NET 41-1011	computed
Coordinates	JobFrame coordinate system — structural (weighted Gower) + content fingerprint	structural@1 · content@2 · pay@2 · semantic@1	computed
Related roles	Nearest neighbors in the structural coordinate space	structural@1	computed

The principles behind the numbers

Ranges, not false precision

Pay is P10 · median · P90, never a single misleading number.

Three honest states, never a fake %

Role matches resolve to confident / review / no-match — a calibrated verdict, not a made-up percentage.

Provenance on every value

Each number carries whether it's observed, computed, projected, or canon — so you always know how much to lean on it.

Gaps shown as gaps

When a figure isn't available at the grain you need, we say so — we don't fabricate a number to fill the cell.

Frozen, versioned editions

Pay and coordinate editions are pinned, so a comparison you make today still means the same thing next quarter.

Privacy by min-N

Any aggregate over people is suppressed below a minimum count, so no individual is identifiable.

Freshness

As of2026-07-30Editioncanon-v2@2.1.0-completeSourcecanon-v2 cell + salary-benchmark (model)