Computational Statistics Expert - PhD

Computational Statistics and Applied Mathematics Expert About the Project

We're building a large-scale benchmark to test how well advanced AI systems can solve hard scientific and engineering problems. As a task designer, you'll create challenging computational problems that check whether AI can use real scientific software to do research-level work — running simulations, interpreting results, designing experiments, and uncovering hidden information from data.

This isn't a typical data-labeling job. You'll design original, graduate-level problems based on real scientific workflows, test them against cutting-edge AI models, and fine-tune them until the difficulty is just right.

You'll create problems that require skilled use of specialized statistical, mathematical, or scientific software packages. Some will ask the AI to compute reproducible numerical answers from a fully defined setup — testing whether it can correctly carry out complex, multi-step workflows. Others will be harder: the AI must plan a series of queries or experiments to uncover information that isn't directly visible, which means thinking strategically about what to measure, how to read partial results, and how to narrow down the possibilities efficiently.

Each problem goes through a testing loop against state-of-the-art AI models, and you'll refine it until it hits the target difficulty.

We welcome statisticians and applied mathematicians working across a wide range of specializations. You do not need experience with every package listed below; strong expertise with one or more specialized computational packages is sufficient.

We're especially interested in experts with deep, hands-on experience using one or more specialized R or Python packages, including examples such as:

• 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, sem Tools, Open Mx

• Topological data analysis: TDAstats, TDApplied

• Differential equations and dynamical systems: de Solve, pomp, FME

• State-space and time-series modelling: KFAS, MARSS, forecast, vars, urca, rugarch, rmgarch, tseries, time Series

• Survival and event-history analysis: survival, flexsurv, timereg, mets

• Mixed, additive, and advanced regression models: lme4, nlme, mgcv, glmmTMB, TMB, quantreg, scam

• Spatial statistics and geostatistics: spatstat, spatstat.geom, spatstat.linnet, spdep, gstat, geoR, sp Bayes, sf, stars, terra, lwgeom

• Statistical learning and specialized modelling: mclust, kernlab, earth, pROC, multcomp, sandwich, effect size, irr

• Optimization and mathematical programming: lp Solve, linprog, nloptr, DEoptimR, SQUAREM

• Numerical linear algebra and high-precision computation: RSpectra, Rmpfr, gmp, pracma

• Computational geometry: geometry, deldir, polyclip

Other similar specialized statistical, mathematical, scientific, or domain-specific R packages will also be considered. Other similar specialized statistical or mathematical Python/Scilab packages are also welcome, such as stats models and PyMC.

Numerical computing and scientific modelling in Matlab/Scilab are also wanted.

You have graduate-level expertise (MS or PhD required; PhD preferred, or MS with 10+ years of relevant experience) in statistics, applied mathematics, or a closely related quantitative field, with real hands-on experience using specialized computational packages — not just theoretical knowledge.

You have written code using one or more specialized statistical, mathematical, or scientific packages to solve actual research or professional problems, and you understand where these tools break, what their edge cases are, and what makes a problem genuinely hard rather than just complicated. Deep expertise with one or more specialized computational packages is more important than familiarity with the entire package list above.

Beyond domain expertise, the best candidates think like puzzle designers: building problems where the challenge comes from smart reasoning rather than…