AI, digital & trading talent in energy

Subsurface Data Scientist Jobs in Energy: Demand & Skills

Part of Plutarch’s ongoing analysis of skill demand in energy hiring — last updated 29 October 2026, based on 7 tracked postings.

As of 29 October 2026, Plutarch tracked 7 unique open Subsurface Data Scientist postings at energy employers.

This is a very small and geographically scattered market, with postings spread across the United States and single roles in several other countries. Almost all titles omit a seniority indicator, with only one title signaling a senior-type level and none signaling junior or executive tracks. The posted titles cover a mix of distinct specialties — geochemistry, reservoir engineering, AI product ownership, data integration and well testing — rather than a single standardized career path.

AI-written summary, 29 October 2026. The figures below are counted from the postings and updated weekly.

Seniority

Only 0 of 7 titles are early career. 1 are senior or above, and 6 state no level.

  • Senior, lead, staff or manager 1
  • Level not stated in title 6

Where

The United States has 3 of 7 postings (43%).

  • United States 3
  • Australia 1
  • France 1
  • India 1
  • Saudi Arabia 1

How many employers state pay

Share of open Subsurface Data Scientist postings that state pay, by country.
CountryPostings we could read that state pay
United States
0 of 3
France
0 of 1
India
0 of 1
Saudi Arabia
0 of 1

Counted from the same open postings as above, leaving out postings whose text we could not read for pay. Pay disclosure tracks local pay-transparency law more closely than employer policy, so a low rate is not evidence that employers here pay less.

Who is hiring

BHP Group Limited has 1 of 7 postings (14%).

Employers with open Subsurface Data Scientist postings, by number of postings.
EmployerOpen postings
BHP Group Limited
1
Diamondback Energy, Inc.
1
Halliburton
1
Kinross Gold
1
Occidental Petroleum Corporation
1
Quest Global
1
TotalEnergies
1

How these roles are titled

Every open posting uses a different title, so there is no standard wording for this role. A search that looks for one title will miss most of this market.

  • AI Product Owner - Geoscience & Reservoir
  • Data Analyst GC Geochemistry | WAIO Geoscience | Mon - Fri
  • Data Scientist or Geoscientist - Débutant
  • IT Subsurface Data Integration Specialist
  • Reservoir Engineer – Digital Solutions Engineer
  • Senior Specialist - Geoscience Data
  • Service well testing - Data Acquisition

What employers ask for

Skills and tools named in the text of 6 open postings we could read in full, most named first.

Python is named in 50% of the postings we read, and Reservoir modelling (Petrel) in 50%.

Skills named in open Subsurface Data Scientist postings, by number of postings naming each.
Skill or toolPostings naming itEmployers
Python
3 of 6
3
Reservoir modelling (Petrel)
3 of 6
3

A skill counts when the posting names it anywhere — requirements, role description or the employer’s own introduction. A skill is shown only when at least 3 postings from at least two employers name it. Postings whose full text we did not read are left out of these counts rather than counted as asking for nothing.

What the postings suggest about filling this role

The pool of open roles is small and spread thin across countries and sub-specialties, so there is little direct head-to-head competition for any single type of role. However, because several postings expect candidates to bridge data science with subsurface or geoscience expertise, filling these specific hybrid roles can be harder than filling a purely technical or purely domain-specific position.

AI-written, 29 October 2026. Read from employer demand only. These sources show what employers advertise, not how many people are available to hire, so this is a reading of the vacancies rather than a measure of scarcity.

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Counts are unique open postings seen in the last three weeks, taken from the named employers’ own published job postings; a vacancy posted in several places is counted once. Employers are named because they published these postings publicly themselves. The written summary is AI-generated from those postings and contains no figures and no employer names. Posting figures are direct counts; pay figures are the median and range of the salaries those employers disclosed. Terms of use.