Bayesian Computation & Problem Design Scientist

Obsidian; San Francisco (CA); On-site; USD 80,000 - 120,000; Part time

Job summary

Obsidian seeks a Computational Bayesian Statistics and Applied Mathematics Expert to design and test challenging computational problems for AI. You will create problems based on real scientific workflows and evaluate them against cutting-edge AI models.

The ideal candidate has a graduate-level background in STEM, strong Python coding skills, and hands-on experience with statistical software. Familiarity with benchmark design and problem-set creation in scientific settings is a bonus.

Qualifications

• Graduate-level expertise in Computational Bayesian Statistics or Applied Mathematics.
• Proven proficiency with at least one scientific software library.
• Strong Python skills and ability to work independently.

Responsibilities

• Design original, graduate-level computational problems.
• Test problems against state-of-the-art AI models.
• Refine problems based on feedback from testing.

Skills

Computational Bayesian Statistics

Applied Mathematics

Python programming

MCMC methods

Numerical PDEs

Education

Graduate-level training in relevant STEM field (MS, PhD)

Tools

PyMC

PyStan

FEniCS

scikit-fem

Job description

Obsidian seeks a Computational Bayesian Statistics and Applied Mathematics Expert to design and test challenging computational problems for AI. You will create problems based on real scientific workflows and evaluate them against cutting-edge AI models.

The ideal candidate has a graduate-level background in STEM, strong Python coding skills, and hands-on experience with statistical software. Familiarity with benchmark design and problem-set creation in scientific settings is a bonus.