Data Scientist (Python & SQL) - Freelance AI Trainer

Overview

Mindrift pairs subject-matter experts with project-based AI work for leading technology companies. The focus is on evaluating, stress-testing, and improving AI systems through short-term, project-based contributions rather than ongoing employment.

Applicants should send a CV in English and clearly mention their English proficiency level.

About the Work

This role centers on creating high-quality data science challenges for AI training and evaluation. Each assignment may differ, but the work typically involves building realistic analytical problems that reflect genuine business use cases across areas such as telecom, finance, government, e-commerce, and healthcare.

• Develop original computational data science problems that mirror real analytical workflows.
• Build Python-based tasks using tools such as Pandas, NumPy, SciPy, scikit-learn, Statsmodels, Matplotlib, and Seaborn.
• Design problems that are too computationally demanding to solve manually in a practical timeframe of days or weeks.
• Construct tasks that require layered reasoning in data preparation, statistical analysis, feature engineering, modeling, and insight generation.
• Create deterministic exercises with reproducible outcomes, avoiding randomness unless fixed seeds are used.
• Ground problems in practical business scenarios such as customer analytics, fraud detection, risk analysis, forecasting, optimization, and operational improvement.
• Cover the full data science workflow from ingestion and cleaning through exploratory analysis, modeling, validation, and deployment considerations.
• Include large-scale data processing situations that call for efficient, scalable approaches.
• Check solutions in Python using established data science libraries and statistical techniques.
• Write clear problem statements with realistic context and verified correct answers.

Candidate Profile

This opportunity is suited to experienced data science professionals who are comfortable with part-time, non-permanent project work.

• At least 5 years of practical data science experience with demonstrated business outcomes.
• A portfolio of completed projects and publications that shows strong real-world problem solving.
• Advanced Python skills for data science work, especially with pandas, numpy, scipy, scikit-learn, and statsmodels.
• Deep knowledge of statistics and machine learning, including both theory and practical application.
• Strong SQL skills and experience working with databases for analysis and manipulation.
• Exposure to GenAI tools such as LLMs, RAG, prompt engineering, and vector databases.
• Awareness of MLOps practices and model deployment processes.
• Familiarity with modern frameworks such as TensorFlow, PyTorch, and LangChain.
• Strong written English at C1 level or above.

How the Engagement Works

The process is: apply, complete the required qualification step(s), join a project, finish assigned tasks, and receive payment.

Time Commitment

During active project phases, the expected workload is about 10 to 20 hours per week. This is only an estimate and is not guaranteed, as the workload depends on the specific project.

Compensation

Contributors on this project may earn up to the equivalent of $55 per hour, depending on skill level and speed of delivery. Pay can vary by project based on scope, complexity, and expertise required.

Additional Information

This is a project-based opportunity and should not be considered a permanent role.

Compensation may differ across other projects available on the platform.