About the job
We invite you to submit your CV in English, including your English proficiency level.
Mindrift is dedicated to connecting specialists with project-based AI opportunities for top-tier technology companies, focusing on the evaluation, testing, and enhancement of AI systems. Please note that participation is project-based and does not constitute permanent employment.
Opportunity Overview
Each project presents distinct tasks, and contributors may:
- Craft advanced statistical problems reflective of professional standards;
- Assess AI solutions for accuracy, underlying assumptions, and limitations;
- Validate computations or simulations utilizing Python libraries such as NumPy, Pandas, SciPy, Statsmodels, and Scikit-learn;
- Enhance AI reasoning to conform with industry-standard logic;
- Implement structured scoring criteria for complex, multi-step problems.
Ideal Candidate Profile
This position is well-suited for statisticians proficient in Python and open to part-time, project-based engagements. Preferred qualifications include:
- A degree in Statistics or a related discipline, such as Probability Theory, Mathematical Statistics, or Applied Statistics;
- 3+ years of professional experience in mathematics;
- Exceptional written English skills (C1/C2 level);
- Proficient in Python for numerical validation;
- A reliable internet connection.
Professional certifications (e.g., PStat, CAP, SAS Certifications) and experience with international or applied projects are advantageous.
How the Process Works
1. Apply → 2. Pass qualifications → 3. Join a project → 4. Complete tasks → 5. Receive payment
Project Commitment
This project requires an estimated commitment of 10–20 hours per week during active phases, subject to specific project requirements. This is an estimate and not a guaranteed workload.
Compensation
- Paid contributions, with rates reaching $39/hour*
- Compensation may be fixed per project or vary based on individual project needs;
- Some projects may include additional incentive payments.
*Note: Rates can vary based on expertise, skills assessment, location, project requirements, and other factors. Highly specialized experts may receive higher rates, while lower rates may apply during onboarding or non-core project phases.

