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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

19-0000

industrial_energy_operations

Coordinates

3 / 4 spaces

pay@2 · 2026-07

What this role is

Manages a team of geophysicists conducting subsurface surveys and analyses to support energy and mining decisions. 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

Runs a geophysics department or project team responsible for seismic processing, reservoir characterization, or environmental geophysics deliverables, owning its operational plan and budget
Designs project schedules across multiple concurrent surveys and reprocessing efforts and ensures teams complete imaging and interpretation work on time
Evaluates diverse subsurface interpretation issues and emerging data trends, deciding on processing/imaging methods where established practice does not resolve the ambiguity
Leads client and cross-functional teams through survey planning, well-tie reviews, and mapping deliverables, and engages clients on project scope and technical results

Knowledge applied: Applies specialized geophysics expertise (seismic processing, reservoir characterization, environmental geophysics) to run a department's operations and budget.
Complexity & problem solving: Evaluates diverse subsurface issues and data trends, selecting imaging/processing methods where established practice is insufficient.
Collaboration & interaction: Leads functional and client-facing teams through survey planning, reviews, and mapping; engages clients on scope and results.
Typical experience: 5–7+ years managing geophysics professionals, schedules, and budgets; often master's or PhD credential with strong systems knowledge.

Skills at this level

Seismic data interpretation: — Interprets seismic data to characterize the subsurface, building quantitative interpretation models and translating imaged reflectors into structural and stratigraphic understanding for decision-making.
Depth imaging and earth model building: — Directs velocity model building and depth imaging workflows — including FWI, subsalt, and OBN techniques — to produce accurate migrated images and earth models for exploration programs.
Reservoir characterization: — Applies quantitative geophysical methods to characterize reservoirs, integrating seismic attributes and well ties to describe subsurface properties for energy programs.
Well ties and mapping: — Performs and reviews well-to-seismic ties and horizon/structure mapping to calibrate seismic interpretation against borehole data.
Geophysical program delivery: — Owns scope, schedule, budget, risk, and governance for multi-workstream seismic and imaging programs, defining KPIs, milestones, and decision gates and reporting progress to company and client leadership.
Geophysical project scheduling: — Designs project schedules across concurrent surveys, reprocessing, and imaging efforts and ensures teams complete geophysical work on time.
ML/AI geophysical workflows: — Sets technical direction for machine-learning and AI-assisted geophysical workflows applied to processing, imaging, and interpretation to raise throughput and image quality.
Client-facing technical authority: — Serves as a senior geophysical authority in client workshops, technical forums, and industry venues (SEG, EAGE), advising on technical strategy and program execution.
Multi-disciplinary team leadership: — Leads teams spanning imaging, processing, R&T, data engineering, and commercial functions, and mentors senior and junior geophysicists to raise capability in FWI, OBN, and subsalt imaging.
Geophysical software proficiency: — Directs use of geophysical interpretation and analysis platforms (e.g., Petrel, Kingdom, GeoFrame, Techlog) to handle and interpret seismic and well data across the team's workflows.
Physics, geology, and mathematics foundation: — Grounds team decisions in the physics, geology, and mathematics underpinning wave propagation and subsurface behavior, essential for guiding imaging and interpretation choices.

Model-authored from 16 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

Geophysics — 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 19-0000 · national

Employment growth 2019–2024, BLS OEWSobserved

-22.9%Geoscientists, Except Hydrologists and Geographers · United States

29,200 → 22,510 jobs

2,000 openings / year, split into:

replacement / exits 1,919

Growth — new jobs (4%)Exits — backfill (96%)

4% of hiring here is genuine expansion; the rest backfills people who move on.

Projected growth 2024–34 expanding: +3.2%
Growth share of hiring: 4%
Exit / retention pressure high: 96%
Openings / year: 2,000

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 Geophysicist	jf_geophysics::E3	$352,063†	confident0.06
Geophysicist	jf_geophysics::P3	$82,468†	confident0.06
Senior Geophysical Technician	jf_geophysics::S3	$49,566†	confident0.06
Senior Geophysics Supervisor	jf_geophysics::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 19-2042

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_geophysics::M3

Geophysics Manager

Geophysics

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: industrial_energy_operations
Function: geoscience
Family: geophysics
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").

Geology · M3review0.17

Geomatics · M3review0.17

Geothermal · M3review0.17

Petrophysics · M3review0.17

Technologist · 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 19-2042	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)