Scaling Global Talent Pipelines for a $338B Texas Energy Powerhouse

Discover How Draup Enabled Workforce Intelligence Modernization for a Global Energy Leader

60,000+

Employee Count

$338 Bn

Revenue

Oil & Gas, Energy

Industry

Spring, Texas

Headquarters

About the Company

A multinational energy enterprise operating across upstream, midstream, and downstream operations. The organization drives large-scale offshore production and technology-enabled energy systems while prioritizing local capability development, digital integration, and resilient talent pipelines for expanding offshore projects.

Before Draup

1

Fragmented Talent Visibility Across Emerging Markets

No integrated view of availability, maturity, cost, or mobility for engineering, technical, and logistics roles, especially in Guyana and India.

2

Rapid Operational Expansion Outpacing Local Talent Pipelines

Offshore growth intensified demand for skilled local talent, but training capacity and readiness data were limited.

3

Unclear Global Hub Prioritization for Corporate & Digital Roles

Leadership lacked a data-backed way to compare hubs like Bangalore, Bangkok, and Buenos Aires on cost, talent depth, and ecosystem maturity.

4

Limited Competitor & Academic Intelligence

Insufficient insights into competitor hiring (SLB, Halliburton, SBM Offshore, Saipem) and inconsistent visibility into university ecosystems across Guyana, India, APAC, and Canada.

The Core Challenges

Limited local specialized talent increased reliance on expatriates and slowed hiring.

Rapid offshore growth, digital integration, and safety demands widened emerging skill gaps.

Strong competition from major energy peers intensified pressure for critical technical talent.

Limited visibility into talent pipelines and mobility trends hindered long-term planning.

The Solution

Petroleum Engineer

PROFILE

LOCATION ANALYSIS

TALENT MARKET INSIGHTS

CAREER PATHS

DATA INTEGRATION

TECH STACK

Unified Talent Intelligence Across Energy Functions

Integrated insights on Geoscience, Engineering, Technical, and Logistics roles—covering availability, maturity, compensation, mobility, and peer activity to guide localized workforce strategy.

Global Hub Prioritization for Digital & Enterprise Roles

ML-driven evaluation of APAC, LATAM, and EMEA markets identified cost-efficient, scalable hubs such as Bangalore, Bangkok, and Buenos Aires.

Standardized University & Early-Career Benchmarking

Assessed graduation volumes, STEM strength, curriculum relevance, and industry engagement to strengthen early-career talent pipelines.

Deep Market Intelligence for Key North American & Emerging Hubs

Provided function-level insights across Calgary, Edmonton, and Georgetown by combining hiring demand, mobility patterns, and competitor benchmarking.

Outcome

Stronger Multi-Market Workforce Planning

The organization could now forecast talent needs across regions more accurately, enabling faster, data-backed decisions for expansion and localization.

Optimized Global Hub Strategy

Leadership gained clarity on which markets offered the best cost–talent balance, allowing more confident investment in Bangalore, Bangkok, and Buenos Aires.

Improved Early-Career and Local Talent Pipelines

Standardized university insights enabled targeted partnerships and training programs, strengthening talent availability in emerging hubs.

Improved Competitive Edge

Better insight into peer activity enabled more accurate forecasting and stronger hiring for critical engineering talent.

Powered by unparalleled global labor & market data

19K+

Skills

850M+

Professionals

5700+

Locations

1.6M+

Peer Group Companies

1B+

Job Descriptions

4M+

Career Paths

Data Trust & Methodology at Draup

Draup uses a structured methodology to reduce inaccuracies and prevent misleading information, with a focus on transparency and responsible data practices.

1

Data Transparency and Coverage

Draup sources data from diverse, vetted global inputs including public datasets, labor information, professional ecosystems, and industry research. Coverage varies by region and role, and we supplement limited areas with alternative sources and analyst-led modeling.

2

Profiles Behind the Numbers

All aggregate insights related to buyers, roles, skills, and locations are grounded in structured, anonymized human and organizational profiles rather than pure extrapolation. This ensures context, reliability, and protection of personal identities.

3

Data Hygiene and Integrity

We maintain a clean, accurate data environment by removing duplicates, outdated entities, and invalid records. Automated checks powered by ML models and analyst reviews work together to ensure clarity and accuracy for every entity, such as buyer, role, skill, technology, or company. Learn more

4

AI Governance and Bias Mitigation

Our governance framework combines automated evaluations with Human-in-the-Loop reviews. We use statistical checks, audits, and cross-source comparisons to reduce demographic and structural bias before insights reach the platform.

5

Compensation Guardrails

Compensation insights use blended, directional inputs such as aggregated ranges, benchmarks, modeled distributions, and market signals. We distinguish clearly between modeled and reported data and apply guardrails to prevent over-interpretation.

6

Hourly and Frontline Workforce Coverage

For hourly, frontline, and blue-collar roles where digital visibility is limited, we incorporate government datasets, localized labor information, and specialized partners to build a more complete and balanced view of workforce supply and demand.