Remote Bayesian Statistics & AI Problem Designer

Mercor; Greater London; On-site; GBP 72,795 - 103,993; Full time

Job summary

Mercor is looking for a Computational Bayesian Statistics and Applied Mathematics Expert to design and develop computational problems that test AI capabilities. This remote contract role offers a flexible commitment of 15–20 hours per week.

Applicants should have graduate-level training in a relevant STEM field and strong skills in Python and scientific software. The position requires independence and proficiency in working with Linux environments.

Qualifications

• Graduate-level training in a relevant STEM field (MS, PhD, or equivalent research experience).
• Proven proficiency with at least one scientific software library (PyMC, FEniCS, etc.).
• Strong Python skills for writing problem setups and solution validators.
• Ability to work independently and refine problem designs.
• Comfortable in a Linux/terminal environment with remote compute sandboxes.
• Available for at least 15–20 hours/week.

Responsibilities

• Design challenging computational problems to test AI capabilities in solving scientific tasks.
• Develop problems requiring specialized scientific software for simulations.
• Refine problems through iterative testing against AI models.
• Collaborate on tasks requiring strategic thinking and efficient information extraction.
• Work independently to enhance problem designs based on feedback.

Skills

Scientific software proficiency

Python programming

Independent work

Linux/terminal skills

Education

Graduate-level training in a relevant STEM field

Tools

PyMC

FEniCS

Job description

Mercor is looking for a Computational Bayesian Statistics and Applied Mathematics Expert to design and develop computational problems that test AI capabilities. This remote contract role offers a flexible commitment of 15–20 hours per week.

Applicants should have graduate-level training in a relevant STEM field and strong skills in Python and scientific software. The position requires independence and proficiency in working with Linux environments.