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Key statistics

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

1 of 8

Track

Individual contributor

Family rungs priced

P1 · P2 · P3 · P4 · P5 · P6 · P7

—

industrial_energy_operations

Coordinates

2 / 4 spaces

pay@2 · 2026-07

What this role is

Entry-level professional who collects and processes earth-science field and lab data under close supervision. At the Entry-Level Professional (P1) level, the role owns own tasks within a defined component, routine problems with known solutions, and drives own deliverables. None — building the craft.

What this level does

Collect rock, soil, and water samples under close supervision using industry-standard techniques, following documented procedures to ensure sample quality and chain-of-custody
Operate common geological field equipment such as GPS and XRF analyzers under direct instruction, escalating unexpected instrument readings to a senior technologist
Perform routine data entry and organization of field observations into MS Excel, Microsoft Access, or a geological database, checking entries against source logs
Learn to recognize and label common rock, soil, and sample types, building the foundational geological knowledge needed to work independently

Knowledge applied: Applies foundational earth-science knowledge and documented field procedures to routine sampling and data-entry tasks; learning to operate GPS and XRF equipment.
Complexity & problem solving: Handles routine problems with standard answers; escalates anything unexpected in instrument readings or sample handling.
Collaboration & interaction: Works within a stable field/lab team, taking direct instruction from senior technologists.
Typical experience: 0–1 years; new graduate or intern in geoscience or a related earth-science field.

Skills at this level

Geological Field Sampling: — Collects rock, soil, and water samples using industry-standard techniques and chain-of-custody discipline so that sample integrity and representativeness are preserved for downstream analysis.
Field Instrumentation Operation: — Operates and troubleshoots geological field equipment including GPS, XRF analyzers, and geophysical instruments, resolving calibration and setup issues in the field.
Geological Database Management: — Structures, populates, and maintains geological data in EarthSoft EQuIS Geology and Microsoft Access, enforcing data standards and correcting inconsistencies across field and lab records.
Geoscience Data Processing: — Processes and quality-checks acquired geological and geophysical data in tools such as Geosoft Oasis montaj and RockWorks, flagging anomalies against expected ranges before interpretation.
Geological Modeling: — Builds subsurface models and cross-sections from field, well, and instrument data using RockWorks, Petra, and Surfer to represent geological structure and support interpretation.
Geological Mapping and GIS: — Produces technical maps and spatial analyses in ESRI ArcGIS and finishes cartographic products in Adobe Illustrator and CorelDraw to communicate geological interpretations.
Seismic Interpretation: — Performs 2D/3D seismic interpretation, attribute analysis, and velocity modeling in industry-standard interpretation software to characterize subsurface structure and reservoir potential.
Investigation Methodology Selection: — Independently develops, conducts, and evaluates complex geological investigations, selecting field acquisition and analytical approaches within program guidelines and standards.
Geochemical Analysis: — Uses geochemical modeling tools such as The Geochemist's Workbench to interpret water and mineral chemistry and predict geochemical processes in the subsurface.
Geoscience Team Leadership: — Serves as lead worker over lower-level geoscience employees, assigning and reviewing work, preparing technical recommendations, and providing guidance and training.
Emerging Geoscience Technology Adoption: — Evaluates emerging geoscience technologies and methodologies and integrates them into standard practice to measurably improve exploration success rates.
Geoscience Program and Vendor Management: — Conceives, plans, and executes complex geoscience programs and manages external consultants and technology providers to deliver technical objectives.
Geoscience Policy and Standards Formulation: — Formulates organizational and national-scale geoscience policies, interpretation standards, and technical roadmaps that shape company and industry practice.

Model-authored from 20 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

Technologist — 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?

Employment growth 2019–2024, BLS OEWSobserved

+11.1%Life, Physical, and Social Science Technicians, All Other · United States

64,260 → 71,400 jobs

10,600 openings / year, split into:

replacement / exits 10,308

Growth — new jobs (3%)Exits — backfill (97%)

3% of hiring here is genuine expansion; the rest backfills people who move on.

Projected growth 2024–34 expanding: +3.5%
Growth share of hiring: 3%
Exit / retention pressure high: 97%
Openings / year: 10,600

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 P1 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$101k

San Francisco-Oakland-Fremont, CA$90k

Washington-Arlington-Alexandria, DC-VA-MD-WV$83k

Boulder, CO$82k

Seattle-Tacoma-Bellevue, WA$82k

Boston-Cambridge-Newton, MA-NH$79k

Lexington Park, MD$77k

Trenton-Princeton, NJ$76k

Pay only — metro-level demand isn't in public data (it needs job postings). National median $60k; 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 P1 (P1 — Entry-Level Professional) from the levels around it — this level only. Every word is real canon for this profile.

Scope: Own tasks within a defined component
Autonomy: Close supervision; work reviewed frequently
Complexity: Routine problems with known solutions
Impact: Own deliverables
Decision rights: Few independent decisions; escalates the rest
Leadership: None — building the craft

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

Promotion

The next rung from P1 is P2.

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, Geoscience Operations	jf_technologist::E1	$169,459†	confident0.06
Geoscience Technologist Supervisor	jf_technologist::M1	$85,619†	confident0.06
Entry Geoscience Technologist	jf_technologist::S1	$37,323†	confident0.06
Geoscience Technologist	jf_technologist::P2	$72,357†	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 19-4099

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

P1

jf_technologist::P1

Associate Geoscience Technologist

Technologist

no content fingerprint

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: P
Superfunction: industrial_energy_operations
Function: geoscience
Family: technologist
Universal level: 1 / 8

Content

content@2

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

No content fingerprint for this profile.

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 · P1review0.17

Geomatics · P1review0.17

Geophysics · P1review0.17

Geothermal · P1review0.17

Petrophysics · P1review0.17

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 19-4099	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)