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Key statistics

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

3 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

Manages the daily operations and staff of an optical practice or retail eyewear location. At the Senior Manager (M3) level, the role owns multiple teams or a sub-function, multi-team execution and resourcing trade-offs, and drives sub-function outcomes. Manages managers and/or several teams.

What this level does

Manage the full operational, staffing, and financial machinery of one optical store, owning the store's optical P&L
Set and monitor store sales targets, analyze customer service trends, and act on gaps in dispensing accuracy or turnaround time
Own hiring, scheduling, training, and performance management for the store's optical staff
Manage vendor relationships and inventory strategy for the location, predicting frame/lens demand from sales data and market trends
Ensure compliance with dispensing standards, prescription verification, and company optical retail policies across the store

Knowledge applied: Applies operational and financial management knowledge to run a full optical store, evaluating diverse issues and sales/service trends.
Complexity & problem solving: Addresses diverse operational, staffing, and inventory issues; evaluates trends in sales and dispensing performance to act.
Collaboration & interaction: Leads the store's optical team and manages vendor and customer-escalation relationships.
Typical experience: 5–7+ years managing optical professionals, inventory, and a store budget.

Skills at this level

Optical retail P&L management: — Owns the target and the strategy to hit it for an optical unit or portfolio, driving profitable store sales and managing the operational and financial machinery behind them.
Optical measurement and dispensing oversight: — Ensures correct measurement of ophthalmic lenses, neutralization and verification of prescriptions via auto and manual lensometry, and proper fit, alignment, and adjustment of dispensed eyewear.
Lens and frame product knowledge: — Maintains current knowledge of retail optical, lens technology, coatings, and diverse optical products to guide recommendations matched to occupational use, sports, and tints.
Lab work-order accuracy: — Governs creation and verification of work orders for ophthalmic laboratory technicians, ensuring lens type, product number, coatings, and measurements are precisely recorded.
Optical inventory and demand planning: — Manages retail optical inventory — ordering frames, lenses and supplies, running inventory checks, and predicting demand from sales data and market trends.
Optical team leadership and development: — Leads, trains, and mentors optical staff and junior optical managers, building talent pipelines and readiness for ABO certification.
Customer service and dispensing communication: — Explains frame and lens options clearly, instructs customers on adapting to and caring for eyewear, and resolves optical service issues at and beyond the counter.
Multi-site optical operations: — Scales operating standards, service offerings, and process foresight from a single store to a cluster, region, or portfolio of optical locations.
Optical service and technology rollout: — Introduces new optical technologies and service offerings and oversees complex projects such as store renovations and lensometry/system upgrades.
Dispensing decision-making: — Decides which lens materials, designs, and adjustments best suit each customer's prescription, preferences, and lifestyle within optical dispensing standards.

Model-authored from 21 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

Optical — 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 M3 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$196k

San Francisco-Oakland-Fremont, CA$175k

Washington-Arlington-Alexandria, DC-VA-MD-WV$162k

Boulder, CO$160k

Seattle-Tacoma-Bellevue, WA$160k

Boston-Cambridge-Newton, MA-NH$153k

Lexington Park, MD$149k

Trenton-Princeton, NJ$148k

Pay only — metro-level demand isn't in public data (it needs job postings). National median $117k; 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 M3 (M3 — Senior Manager) from the levels around it — this level only. Every word is real canon for this profile.

Scope: Multiple teams or a sub-function
Autonomy: Sets goals within functional strategy
Complexity: Multi-team execution and resourcing trade-offs
Impact: Sub-function outcomes
Decision rights: Owns goals, budget input, and people decisions across teams
Leadership: Manages managers and/or several teams

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

Promotion

The next rung from M3 is M4.

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
Chief Vision Care Officer	jf_optical::E3	$352,063†	confident0.06
Optometrist	jf_optical::P3	$82,468†	confident0.06
Senior Optical Assistant	jf_optical::S3	$49,566†	confident0.06
Senior Supervisor, Optical	jf_optical::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

M3

jf_optical::M3

Optical Manager

Optical

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: optical
Universal level: 3 / 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 · M3review0.17

Loss Prevention · M3review0.17

Pharmacy · M3review0.17

Product Specialty · M3review0.17

Store Management · M3review0.17

General · M3review0.17

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)