Remote Math & Computational Research Specialist

Gramian Consulting Group; United States; Remote; USD 69,000 - 124,000; Part time

Job summary

Gramian Consulting Group is seeking highly skilled Mathematics Experts to contribute to an AI training initiative. You will solve, develop, review, and validate complex mathematical tasks using Python and command-line workflows, translating concepts into executable procedures and algorithms.

Strong mathematical knowledge and practical Python experience are essential. The role focuses on numerical experiments, parameter sweeps, validation, and the development of reproducible computational

Qualifications

• MSc/PhD in mathematics or a closely related quantitative field is required.
• Strong practical Python skills with experience in scientific computing.
• Ability to translate complex mathematical concepts into executable workflows.

Responsibilities

• Solve and validate advanced mathematical and computational problems across relevant disciplines.
• Translate requirements into precise, executable workflows.
• Implement mathematical equations and numerical procedures using Python and command-line tools.
• Automate calculations, numerical experiments, and result validation.
• Review outputs for correctness and reproducibility.
• Document methodologies, calculations, and results clearly.
• Support AI training initiatives with rigorous mathematical solutions.
• Complete MSc/PhD and demonstrate strong practical Python skills.

Skills

Advanced mathematics

Python programming

Computational problem-solving

Education

MSc/PhD in mathematics or related field

Tools

Command-line tools

Job description

Gramian Consulting Group is seeking highly skilled Mathematics Experts to contribute to an AI training initiative. You will solve, develop, review, and validate complex mathematical tasks using Python and command-line workflows, translating concepts into executable procedures and algorithms.

Strong mathematical knowledge and practical Python experience are essential. The role focuses on numerical experiments, parameter sweeps, validation, and the development of reproducible computational