Applied Mathematician or Statistician, R/Python/Matlab/Scilab

Design graduate-level computational challenges that evaluate whether advanced AI systems can perform research-grade scientific and engineering work, including simulations, result interpretation, experimental design, and extracting hidden information from data. This role focuses on original problem design, rigorous testing, and calibrating challenge difficulty for advanced AI models.

You will create computational statistics and applied mathematics problems grounded in real scientific workflows. Some tasks require reproducible numerical answers from fully specified, multi-step setups. Others require an AI system to strategically plan queries or experiments, interpret partial results, select useful measurements, and efficiently narrow possible answers.

• Design original, challenging computational problems using specialized statistical, mathematical, or scientific software.
• Create problems that test simulations, analysis of results, experimental design, and information discovery from data.
• Write problem setups, oracle functions, and solution validators in Python.
• Test problems against advanced AI models and refine them through iterative evaluation until they reach the intended difficulty.
• Develop challenges where careful reasoning, rather than raw computation or surface-level pattern matching, determines the correct solution.

Deep hands-on expertise with one or more specialized R, Python, Matlab, or Scilab packages is sufficient. Relevant areas include Bayesian statistics; item response theory and psychometrics; structural equation and latent-variable modeling; topological data analysis; differential equations and dynamical systems; state-space and time-series modeling; survival and event-history analysis; mixed, additive, and advanced regression; spatial statistics and geostatistics; statistical learning; optimization and mathematical programming; numerical linear algebra and high-precision computation; and computational geometry.

Examples of relevant R packages include rstan, cmdstanr, rjags, runjags, brms, rstanarm, nimble, bayesplot, posterior, loo, TAM, sirt, mirt, lavaan, semTools, OpenMx, TDAstats, TDApplied, deSolve, pomp, FME, KFAS, MARSS, forecast, vars, urca, rugarch, survival, flexsurv, lme4, nlme, mgcv, glmmTMB, spatstat, spdep, gstat, geoR, spBayes, sf, terra, mclust, kernlab, lpSolve, nloptr, RSpectra, Rmpfr, geometry, deldir, and polyclip. Comparable specialized statistical, mathematical, scientific, or domain-specific packages are also relevant, including Python tools such as statsmodels and PyMC. Numerical computing and scientific modeling experience in Matlab or Scilab is also valued.

• MS or PhD in statistics, applied mathematics, a related quantitative STEM field, or equivalent research experience. A PhD is preferred; candidates with an MS should have 10 or more years of relevant experience.
• Demonstrated proficiency with at least one specialized computational package through research publications, open-source contributions, or professional work.
• Practical experience applying specialized statistical, mathematical, or scientific software to real research or professional problems, including understanding tool limitations and edge cases.
• Strong Python programming skills.
• Ability to work independently and improve problem designs in response to feedback.
• Comfort working in a Linux terminal environment and with remote compute sandboxes.

• Experience spanning multiple computational domains or specialized software packages.
• Familiarity with benchmark or evaluation design.
• Experience teaching scientific subjects or creating exams and problem sets.
• Experience with computational reproducibility and containerized environments.

• Remote, hourly engagement.
• Availability of at least 15 to 20 hours per week is required.

$70 to $90 per hour.