Remote | Computational Bayesian Statistics & Applied Mathematics Consultant — $60–$90/hour

We are sharing a specialised part-time consulting opportunity for computational Bayesian statistics and applied mathematics professionals experienced in PyMC, PyStan, PyJAGS, CmdStanPy, numerical PDE tools, finite element and finite difference methods, computational topology, differential algebra, Python scientific computing, and benchmark evaluation workflows.

This role supports current and upcoming remote consulting opportunities focused on computational statistics problem design, applied mathematics workflow evaluation, scientific software review, Python-based validation, and high-quality project execution. Selected professionals will design challenging research-grade computational problems, create precise expected outputs, develop validation logic, and refine task difficulty based on structured calibration feedback.

Key Responsibilities

Professionals in this role may contribute to:

Bayesian Statistics & Applied Mathematics Problem Design

• Design original graduate-level computational problems grounded in Bayesian statistics, applied mathematics, numerical analysis, computational topology, differential algebra, or related scientific workflows
• Create tasks involving MCMC, Bayesian modelling, posterior inference, finite element methods, finite difference methods, mesh-based numerical modelling, numerical PDEs, or specialised Python-based computational methods
• Develop problems that require precise outputs from fully specified computational setups
• Design tasks that test strategic reasoning, experimental planning, interpretation of partial outputs, and careful mathematical analysis

Scientific Software & Computational Workflow Evaluation

• Work with Bayesian statistics libraries such as PyMC, PyStan, PyJAGS, and CmdStanPy
• Work with applied mathematics and numerical PDE tools such as FEniCS, FEniCSx, DOLFINx, scikit-fem, FiPy, Devito, and Dedalus
• Create or review tasks involving computational topology tools such as GUDHI or differential algebra tools such as DACEyPy
• Evaluate whether tasks require genuine mathematical and computational reasoning rather than surface-level implementation

Python-Based Validation & Benchmark Calibration

• Write Python problem setups, oracle functions, and solution validators
• Refine tasks through calibration feedback until the difficulty reaches the target range
• Create structured evaluation materials that support reproducible scoring and consistent review
• Maintain technical accuracy, reproducibility, and clear documentation across submitted task materials

Ideal Profile

Strong candidates may have:

• Graduate-level expertise in computational Bayesian statistics, applied mathematics, statistics, numerical analysis, computational science, mathematical modelling, or a closely related STEM field
• MS, PhD, or equivalent research experience in a relevant domain
• Hands-on experience using at least one relevant scientific software library in real research or professional work
• Demonstrated proficiency evidenced by research publications, open-source contributions, professional projects, or applied scientific computing work
• Strong Python programming skills for scientific computing, validation logic, and task setup
• Ability to understand software edge cases, numerical limitations, and what makes a problem genuinely difficult
• Comfort working independently and iterating on problem designs based on feedback
• Ability to work in Linux, terminal-based environments, or remote compute sandboxes

Educational Background

• MS, PhD, or equivalent research experience in statistics, applied mathematics, mathematics, computer science, computational science, physics, engineering, data science, or a related technical field is preferred
• Research publications, open-source contributions, professional scientific software work, or applied computational modelling experience are highly relevant
• Experience designing or validating computational statistics, Bayesian inference, numerical PDE, finite element, topology, or applied mathematics workflows may be especially valuable

Nice to Have

• Experience with multiple listed domains or tools across Bayesian statistics, numerical PDEs, finite element methods, finite difference methods, computational topology, or differential algebra
• Familiarity with benchmark design, evaluation design, calibration workflows, or technical task validation
• Background in scientific pedagogy, exam design, problem-set creation, or graduate-level technical assessment
• Experience with computational reproducibility, containerized environments, remote compute, or research software packaging
• Availability for approximately 15–20 hours per week depending on project scope

Why This Opportunity

• Apply computational Bayesian statistics and applied mathematics expertise to structured remote scientific problem design work
• Contribute to high-quality benchmark tasks involving Bayesian inference, numerical PDEs, finite element methods, computational topology, differential algebra, and Python validation
• Work on flexible assignments aligned with your computational statistics, applied mathematics, or scientific software background
• Use your ability to design rigorous, strategic, research-grade computational problems
• Remote structure with competitive hourly compensation

Contract Details

• Independent contractor role
• Fully remote with flexible scheduling
• Eligible professionals may be based in approved project locations depending on project needs
• Expected commitment of approximately 15–20 hours per week depending on project availability
• Competitive rates between $60–$90 per hour depending on expertise and project scope
• Weekly payments via Stripe or Wise
• Projects may be extended, shortened, or adjusted depending on scope and performance
• Work will not involve access to confidential or proprietary information from any employer, client, or institution

About the Platform

This opportunity is available through 24-MAG LLC. We connect experienced professionals with remote consulting opportunities across technical, evaluation, and project-based workstreams.

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