Applied Mathematics & Scientific Computing Specialist

About Gramian

Gramian Consultancy is a boutique consultancy specializing in IT professional services and engineering talent solutions. With a strong background in software engineering and leadership, we help companies build high-performing teams by matching them with professionals who truly fit their needs.

About the Role

We are looking for a Mathematics expert with strong scientific programming skills to develop realistic, terminal-based computational tasks for an advanced AI benchmarking project.

You will translate authentic mathematical and research workflows into reproducible environments that evaluate whether AI agents can formulate mathematical problems, implement algorithms, debug numerical workflows, and produce verifiable computational results.

The role combines expertise in applied mathematics, numerical analysis, optimization, statistics, probability, and mathematical modeling with hands-on computational problem-solving and automated evaluation.

Core Domains

Applied Mathematics, Numerical Analysis, Optimization, Statistics, Probability, Mathematical Modeling, Differential Equations, Dynamical Systems, Operations Research, and Computational Geometry.

Key Responsibilities

Design authentic, multi-step computational mathematics tasks based on real-world research and mathematical workflows.

Translate mathematical problems into self-contained, reproducible terminal environments.

Prepare datasets, equations, model definitions, constraints, initial conditions, and expected outputs.

Implement expert reference solutions using Python, R, Julia, C/C++, Bash, or other relevant tools.

Develop tasks involving optimization, numerical integration, differential equations, matrix computation, statistical inference, stochastic modeling, and algorithm analysis.

Define rigorous grading criteria covering numerical accuracy, convergence, complexity, feasibility, and mathematical correctness.

Establish appropriate numerical tolerances, stopping criteria, stability requirements, and reproducibility controls.

Develop automated tests that validate results across edge cases and alternative valid implementations.

Debug issues involving floating-point precision, solver failures, conditioning, convergence, and performance.

Document mathematical formulations, assumptions, expected outputs, and known limitations.

Requirements

Advanced academic or professional expertise in Mathematics, Applied Mathematics, Statistics, Computational Mathematics, or a closely related quantitative discipline.

Strong programming skills in Python, R, Julia, C/C++, Bash, or another relevant scientific programming language.

Hands-on experience with numerical analysis, optimization, statistics, mathematical modeling, or computational mathematics.

Experience implementing and validating mathematical algorithms or computational models.

Ability to work independently in Linux or terminal-based environments.

Strong understanding of numerical precision, convergence, stability, and mathematical correctness.

Experience developing, debugging, and validating reproducible computational workflows.