Senior Data Scientist – Energy, Oil & Gas

Raw Ventures

Senior Data Scientist handling end-to-end data science for energy sector with direct access to founders. Responsible for P&L reconstruction, model development, and financial data analysis.

Posted 7/30/2026full-timeRemote • 🇦🇪 United Arab EmiratesSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates extensive expertise in data science with a focus on financial data analysis, model development, and production deployment. Proficient in Python and SQL, with a strong background in the oil and gas industry, particularly in trading and logistics.

Highest-signal resume keywords

Data Science OwnershipPython ProgrammingSQL ProficiencyOil & Gas Industry ExperienceModel Production Deployment

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Data ScienceModel DevelopmentAnomaly DetectionFraud DetectionP&L ReconstructionMargin AttributionFinancial Data AnalysisData Measurement

Industry Keywords

Oil & GasTradingRefiningLogisticsStorageDistributionFinanceRisk Management

Tech Stack

Tools & technologies

PythonSQL

About the role

Key responsibilities & impact

A senior individual contributor owning the data science work end to end: deciding what to measure, getting the data, building the models, defending the conclusions.
Work directly with traders, deal economists, finance and treasury.
P&L reconstruction and margin attribution across the deal lifecycle.
Anomaly and fraud detection on financial data.
The reporting layer for finance and risk — margin, cost per tonne, exposure, receivables, cash position.

Requirements

What you’ll need

7+ years in data science, with senior ownership of your own workstreams
Oil & gas industry experience — at least two years of delivered projects in trading, refining, logistics, storage or distribution.
Strong Python and SQL, and readiness to work in raw data yourself: unfamiliar schemas, undocumented tables, exports never meant for analysis
Experience taking models into production and operating them; close to essential for us
Professional proficiency in Russian

Benefits

Comp & perks

Ownership of the data science work, and room to shape how it is done
Direct access to founders and decision-makers; short feedback loops
Generous AI tooling budget — frontier models are a working expense, not a perk
Competitive compensation, discussed individually