Computational Bayesian Statistics and Applied Mathematics Expert

Accountabilities:

• Design original graduate-level computational problems that evaluate AI systems on advanced scientific and engineering workflows.
• Develop realistic research scenarios involving Bayesian statistics, applied mathematics, numerical simulations, optimization, and related computational methods.
• Create problem setups, Python-based validation scripts, oracle functions, and evaluation criteria to accurately assess AI-generated solutions.

Requirements:

• Master's degree, PhD, or equivalent research experience in Applied Mathematics, Statistics, Computer Science, Engineering, or another relevant STEM discipline.
• Strong hands-on experience with at least one specialized scientific library such as PyMC, PyStan, PyJAGS, CmdStanPy, FEniCS, FEniCSx, DOLFINx, scikit-fem, FiPy, Devito, Dedalus, GUDHI, DACEyPy, lmfit, or comparable research tools.
• Practical expertise in areas such as Bayesian modeling, MCMC, finite element or finite difference methods, computational topology, differential algebra, optimization, or related computational mathematics techniques.

Benefits:

• Fully remote contractor position with flexible working hours.
• Competitive hourly compensation ranging from $70–$100 USD per hour.
• Weekly payments through Stripe or Wise.