Data Scientist (Python & SQL) - Freelance AI Trainer

Role overview

Mindrift brings together subject-matter experts and project-based AI work for major technology organizations. The work centers on evaluating, testing, and refining AI systems. This is not a permanent employment role; participation happens on a project basis.

What you may do

• Build original computational data science challenges that reflect real analytical work across sectors such as telecom, finance, public services, e-commerce, and healthcare.
• Develop Python-based tasks that make use of libraries such as Pandas, NumPy, SciPy, scikit-learn, Statsmodels, Matplotlib, and Seaborn.
• Design work that is computationally demanding and not realistically solvable by hand in a short time.
• Create tasks that require multi-step reasoning in areas like data preparation, statistical analysis, feature engineering, predictive modeling, and insight generation.
• Keep exercises deterministic by avoiding random variability, or by using fixed seeds so answers can be reproduced exactly.
• Ground problems in practical business scenarios such as customer analytics, risk evaluation, fraud detection, forecasting, optimization, and efficiency improvements.
• Construct end-to-end assignments that cover the full data science workflow from ingestion and cleaning through EDA, modeling, validation, and deployment considerations.
• Include large-scale data processing cases that call for scalable computational methods.
• Check solutions using Python, standard data science tooling, and statistical techniques.
• Write clear problem statements with realistic business context and verified answers.

What the role calls for

This is suited to data science professionals with Python experience who are open to part-time, non-permanent project work.

• At least 5 years of practical data science experience with demonstrated business impact.
• A portfolio of completed work and publications that show strong real-world problem solving.
• Advanced Python skills for data science, including pandas, numpy, scipy, scikit-learn, and statsmodels.
• Strong background in statistics and machine learning, including solid understanding of algorithms, methods, and where to apply them.
• Advanced SQL skills and database operations for analysis and manipulation.
• Exposure to GenAI tools such as LLMs, RAG, prompt engineering, and vector databases.
• Understanding of MLOps and model deployment processes.
• Familiarity with modern frameworks such as TensorFlow, PyTorch, and LangChain.
• Very strong written English at C1 level or above.

Process and working pattern

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

During active periods, the expected workload is about 10 to 20 hours per week. This is only an estimate and is not guaranteed; it applies only while a project is running.

Compensation

Contributors can earn up to the hourly equivalent of USD 58, depending on their level and pace of work. Pay can vary from one project to another based on scope, complexity, and expertise required.

Application note

Please share your CV in English and state your English proficiency level.