Computational Mathematician – Scientific Computing

24-MAG

PhD computational mathematician designing numerical linear algebra, mechanics, and finance problems for 24-MAG. Building Python/R solutions, grading criteria, and reproducible tests.

Posted 8/25/2026part-timeRemote • New York • 🇺🇸 United StatesMid-LevelSenior💰 $60 per hourWebsite

Core Competencies

Role fit

Core Competencies

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Expertise in developing computational mathematics problems and tasks, with a strong focus on numerical linear algebra, computational mechanics, and computational finance. Proficient in Python or R for scientific programming and capable of maintaining reproducible code and documentation.

Highest-signal resume keywords

PhD In MathematicsNumerical Linear AlgebraComputational MechanicsComputational FinancePython Or R Proficiency

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Computational MathematicsNumerical AnalysisMathematical ModellingSimulationQuantitative ResearchMatrix MethodsNumerical SolversLarge-Scale Linear SystemsAutomated Testing WorkflowsScientific Validation

Soft Skills

Strong Written CommunicationAbility To Document Precisely

Tools & Technologies

GitGitHubDockerContainerised Environments

Industry Keywords

Computational EfficiencyNumerical StabilityComputational ReproducibilityGrading CriteriaFailure Modes

Tech Stack

Tools & technologies

DockerPython

About the role

Key responsibilities & impact

Develop original research-level computational mathematics problems
Build tasks from published papers, public datasets, open-source repositories, or independently designed scenarios
Create multi-step problems reflecting realistic scientific or quantitative workflows
Design tasks with precise, reproducible, mathematically defensible solutions
Develop computational tasks involving matrix methods, numerical solvers, decompositions, and large-scale linear systems
Address numerical stability, conditioning, convergence, computational efficiency, edge cases, and failure modes
Create computational mechanics modelling and simulation tasks involving physical systems, discretisation, and equation solving
Develop computational finance tasks involving simulation, optimisation, pricing, and quantitative risk analysis
Write and validate scientific programming workflows in Python or R
Develop numerical implementations, reference calculations, solution validators, and reproducible automated-testing workflows
Produce authoritative reference solutions and grading criteria
Test tasks against advanced computational systems, analyse failure modes, and calibrate difficulty
Work through Git/GitHub pull-request workflows and Docker-based environments
Respond to automated quality checks and reviewer feedback
Maintain clean, reproducible code and supporting documentation

Requirements

What you’ll need

PhD required in Mathematics, Applied Mathematics, Computational Mathematics, or a closely related field
Demonstrated depth in at least two of: Numerical linear algebra, Computational mechanics, Computational finance
Strong working proficiency in Python or R
Hands-on experience using programming for mathematical modelling, numerical analysis, simulation, or quantitative research
Comfortable with Git/GitHub
Experience running code in Docker or other containerised environments
Strong understanding of numerical accuracy, computational reproducibility, and scientific validation
Ability to translate advanced mathematical concepts into clearly defined computational problems
Strong written communication and ability to document mathematical assumptions, methods, and solutions precisely
H1-B and STEM OPT support is unavailable for this engagement

Benefits

Comp & perks

Part-time independent contractor engagement
Fully remote
20+ hours per week
Initial duration of approximately 6 weeks
Immediate start
Projects may be extended, shortened, or concluded based on project needs and performance