Computational Statistician and Applied Mathematician

Design graduate-level computational challenges that test advanced AI systems on real scientific and engineering workflows, including simulations, result interpretation, experimental design, and extracting hidden information from data. This role focuses on creating original, research-oriented problems, testing them with advanced models, and refining them to the intended level of difficulty. Role Overview

You will build tasks involving specialized statistical, mathematical, and scientific software. Some tasks require reproducible numerical answers from fully specified, multi-step workflows. Others require strategic planning of queries or experiments to reveal information that is not directly observable, using partial results to determine what to measure and how to narrow possibilities efficiently.

• Create original computational problems based on real scientific workflows.
• Design tasks that require advanced use of specialized software packages.
• Develop problems involving simulations, data interpretation, experiment design, and research-level analysis.
• Test each problem against advanced AI models and revise it through an iterative evaluation loop.
• Write Python-based problem setups, oracle functions, and solution validators.
• Build challenges where careful reasoning, rather than surface-level pattern matching or raw computation alone, is needed to reach the correct answer.

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

Deep expertise in one or more specialized packages is sufficient; familiarity with every package is not required. Relevant R and Python expertise includes Bayesian statistics: rstan, cmdstanr, rjags, runjags, brms, rstanarm, nimble, bayesplot, posterior, loo; item response theory and psychometrics: TAM, sirt, mirt, mirtCAT, eRm, ltm, lordif, psych; structural equation and latent-variable modelling: lavaan, semTools, OpenMx; topological data analysis: TDAstats, TDApplied; differential equations and dynamical systems: deSolve, pomp, FME; state-space and time-series modelling: KFAS, MARSS, forecast, vars, urca, rugarch, rmgarch, tseries, timeSeries; survival and event-history analysis: survival, flexsurv, timereg, mets; mixed, additive, and advanced regression: lme4, nlme, mgcv, glmmTMB, TMB, quantreg, scam; spatial statistics and geostatistics: spatstat, spatstat.geom, spatstat.linnet, spdep, gstat, geoR, spBayes, sf, stars, terra, lwgeom; statistical learning and specialized modelling: mclust, kernlab, earth, pROC, multcomp, sandwich, effectsize, irr; optimization and mathematical programming: lpSolve, linprog, nloptr, DEoptimR, SQUAREM; numerical linear algebra and high-precision computation: RSpectra, Rmpfr, gmp, pracma; and computational geometry: geometry, deldir, polyclip.

Comparable specialized statistical, mathematical, scientific, or domain-specific R packages are also relevant. Experience with specialized Python or Scilab packages, including statsmodels and PyMC, is welcome, as is numerical computing and scientific modelling in Matlab or Scilab.

• Experience across multiple computational domains or specialized software packages.
• Familiarity with benchmark or evaluation design.
• Scientific teaching experience or experience designing exams or problem sets.
• Experience with computational reproducibility and containerized environments.

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

$70 to $90 per hour.