Statistician with Python - Freelance AI Trainer

Please submit your CV in English and indicate your level of English proficiency.

Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems. Participation is project-based, not permanent employment.

What this opportunity involves

While each project involves unique tasks, contributors may:

• Design rigorous statistics problems reflecting professional practice;
• Evaluate AI solutions for correctness, assumptions, and constraints;
• Validate calculations or simulations using Python (NumPy, Pandas, SciPy, Statsmodels, and Scikit-learn);
• Improve AI reasoning to align with industry-standard logic;
• Apply structured scoring criteria to multi-step problems.

What we look for

This opportunity is a good fit for statisticians with an experience in python open to part-time, non-permanent projects. Ideally, contributors will have:

• Degree in Statistics or related fields, e.g. Probability Theory, Mathematical Statistics, Applied Statistics, etc.
• 3+ years of professional mathematics experience
• Strong written English (C1/C2)
• Strong Python proficiency for numerical validation
• Stable internet connection

Professional certifications (e.g., PStat, CAP, SAS Certifications) and experience in international or applied projects are an advantage.

How it works

Apply → Pass qualification(s) → Join a project → Complete tasks → Get paid

Project time expectations

For this project, tasks are estimated to require around 10–20 hours per week during active phases, based on project requirements. This is an estimate, not a guaranteed workload, and applies only while the project is active.

Compensation

On this project, contributors can earn up to $43 per hour equivalent , depending on their level and pace of contribution.

Compensation varies across projects depending on scope, complexity, and required expertise. Please note that other projects on the platform may offer different earning levels based on their requirements.