Remote | Statistical Computing & Applied Mathematics Expert — $60–$80/hour

We are sharing a specialised part-time consulting opportunity for experienced statisticians and applied mathematicians with graduate-level expertise in computational methods, numerical modelling, and specialised scientific software. This role supports research into advanced computational problem solving. Selected experts will design original graduate-level problems based on realistic statistical and mathematical workflows, develop reproducible reference solutions, test problem difficulty, and create tasks requiring sophisticated use of R, Python, MATLAB, Scilab, and other specialised computational tools. Key Responsibilities Computational Problem Design Create original graduate-level problems in statistics, applied mathematics, and related quantitative disciplines Develop tasks requiring multi-step computational reasoning rather than straightforward formula application Design reproducible problems with clearly defined inputs, outputs, and validation criteria Create scenarios requiring strategic experimentation, numerical investigation, or inference from partial results Refine problem designs based on testing and feedback Statistical Modelling & Inference Develop problems involving Bayesian statistics, advanced regression, latent-variable models, and statistical learning Work with specialised packages such as rstan, cmdstanr, brms, PyMC, statsmodels, lavaan, OpenMx, lme4, mgcv, glmmTMB , and related tools Design workflows involving parameter estimation, model comparison, diagnostics, and uncertainty quantification Evaluate whether computational methods are appropriate for the statistical problem being solved Incorporate realistic edge cases and numerical limitations Time Series, Survival & Dynamical Systems Create computational tasks involving time-series modelling, state-space methods, and stochastic processes Develop problems using tools such as KFAS, MARSS, forecast, rugarch, rmgarch , and related packages Work with survival and event-history analysis using packages such as survival, flexsurv, timereg , and mets Design differential-equation and dynamical-system workflows using tools such as deSolve, pomp , and FME Evaluate numerical stability, modelling assumptions, and interpretation of results Spatial, Geometric & Specialised Methods Develop problems in spatial statistics, geostatistics, and geographic data analysis Work with packages such as spatstat, spdep, gstat, geoR, spBayes, sf, stars, terra , and related tools Create tasks involving computational geometry, topology, or specialised quantitative analysis Apply tools such as TDAstats, geometry, deldir , and polyclip where relevant Design problems that require careful interpretation of multidimensional or spatial results Optimisation & Numerical Computing Create tasks involving optimisation, mathematical programming, and numerical computation Apply tools such as nloptr, lpSolve, DEoptimR, SQUAREM , or comparable packages Develop problems involving numerical linear algebra and high-precision computation Work with libraries such as RSpectra, Rmpfr, gmp , and pracma Evaluate convergence behaviour, numerical precision, solver selection, and computational efficiency Scientific Programming & Validation Write problem setups, oracle functions, and solution validators Develop reproducible computational workflows using Python and R Apply MATLAB or Scilab for numerical modelling and scientific computation where relevant Verify that expected outputs are mathematically and computationally correct Document assumptions, parameters, dependencies, and validation logic clearly Problem Testing & Quality Assurance Test tasks against advanced computational systems Identify whether problems are too easy, overly ambiguous, or computationally impractical Refine tasks until they achieve the intended difficulty level Design challenges where strong reasoning is required to distinguish between multiple plausible approaches Ensure problems reward genuine quantitative understanding rather than surface-level pattern matching Ideal Profile Master's degree or PhD in Statistics, Applied Mathematics , or a closely related quantitative field PhD preferred, or a Master's degree combined with substantial relevant professional or research experience Deep hands-on expertise with at least one specialised statistical, mathematical, or scientific software package Demonstrated computational work through research publications, professional projects, or open-source contributions Strong Python programming skills Strong experience with R , MATLAB, Scilab, or comparable numerical computing environments Practical understanding of numerical methods, modelling assumptions, convergence, diagnostics, and computational limitations Ability to design rigorous quantitative problems and independently verify solutions Comfortable working in Linux and terminal-based environments Strong written communication and ability to explain complex quantitative reasoning clearly Experience with scientific teaching, problem-set design, computational reproducibility, or structured evaluation is advantageous Engagement Details Part-time independent contractor engagement Fully remote Expected commitment of at least 15–20 hours per week Flexible scheduling based on project requirements Compensation: $60–$80/hour Work involves computational problem design, reference-solution development, validation, and iterative testing Projects may be extended, shortened, or concluded based on project needs and performance Work must be completed without using confidential or proprietary information belonging to any employer, client, institution, or other third party H1-B and STEM OPT support is unavailable for this engagement About the Platform This opportunity is available through 24-MAG LLC. 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