Data Scientist - Remote in San Francisco

• Analyze large datasets to extract insights that inform model design and refinement.
• Develop and apply statistical models, machine learning techniques, and evaluation frameworks.
• Conduct performance assessments and data quality reviews on AI model outputs.
• Build reusable tools for data processing, visualization, and reporting.
• Partner with research teams to test hypotheses and scale experimental workflows.

• 3+ years of experience in data science, machine learning, or a related analytical field.
• Proficiency in Python and key data science libraries (e.g., pandas, NumPy, scikit-learn).
• Familiarity with large-scale data environments and version-controlled workflows.
• Strong command of statistical analysis, experimental design, and model validation.
• Clear written communication and comfort collaborating across technical teams.

More About the Opportunity

• Expected commitment: 10+ hours/week.
• Exposure to high-impact AI research and applied experimentation.

We consider all qualified applicants without regard to legally protected characteristics and provide reasonable accommodations upon request.

Contract and Payment Terms

• You will be engaged as an independent contractor.
• This is a fully remote role that can be completed on your own schedule.
• Projects can be extended, shortened, or concluded early depending on needs and performance.

Energy Jobline is the largest and fastest growing global Energy Job Board and Energy Hub. We have an audience reach of over 7 million energy professionals, 400,000+ monthly advertised global energy and engineering jobs, and work with the leading energy companies worldwide. We focus on the Oil & Gas, Renewables, Engineering, Power, and Nuclear markets as well as emerging technologies in EV, Battery, and Fusion. We are committed to ensuring that we offer the most exciting career opportunities from around the world for our jobseekers., Leading AI lab to engage independentData Science Expertsfor diverse research and development initiatives. These contractors will support data-driven model improvement, performance evaluation, and applied experimentation.