Data Scientist (Python & SQL) - Freelance AI Trainer

Overview

Mindrift brings together subject-matter experts for project-based AI work with leading technology companies. The focus is on evaluating, testing, and improving AI systems. This is not a permanent employment role; participation is assigned on a per-project basis.

Please share your CV in English and mention your English proficiency level when applying.

What the role involves

Depending on the project, contributors may take on a range of advanced data science tasks, including:

• Building original computational data science challenges that reflect realistic analytical work across sectors such as telecom, finance, public services, e-commerce, and healthcare.
• Writing problems that must be solved with Python using libraries such as Pandas, NumPy, SciPy, scikit-learn, Statsmodels, Matplotlib, and Seaborn.
• Making sure the tasks are computationally demanding and not feasible to solve manually in a short time; they may require days or weeks of effort.
• Creating problem sets that involve multi-step reasoning in areas like data preparation, statistical evaluation, feature engineering, predictive modeling, and insight generation.
• Designing deterministic tasks with reproducible outputs by avoiding randomness or by using fixed random seeds.
• Using realistic business scenarios such as customer analytics, risk evaluation, fraud detection, forecasting, optimization, and operational improvement.
• Developing end-to-end data science workflows covering ingestion, cleaning, exploratory analysis, modeling, validation, and deployment considerations.
• Including scenarios that involve large-scale data processing and require efficient, scalable approaches.
• Checking solutions with Python-based data science tools and statistical techniques.
• Writing clear problem statements with believable business context and validated correct answers.

Candidate profile

This opportunity suits experienced data science professionals who are comfortable with Python and interested in part-time, non-permanent project work. The ideal contributor will bring:

• At least 5 years of practical data science experience with clear business results.
• A strong portfolio of completed work and publications that demonstrate applied problem-solving.
• Advanced Python skills for data science, including pandas, numpy, scipy, scikit-learn, and statsmodels.
• Deep knowledge of statistics and machine learning, along with an understanding of how methods and algorithms are applied in practice.
• Strong SQL ability and experience working with databases for manipulation and analysis.
• Exposure to GenAI tools and workflows such as LLMs, RAG, prompt engineering, and vector databases.
• Familiarity with MLOps and model deployment processes.
• Working knowledge of modern frameworks such as TensorFlow, PyTorch, and LangChain.
• Excellent written English at C1 level or above.

How the project process works

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

Time commitment and compensation

During active project periods, the expected workload is approximately 10 to 20 hours per week. This is only an estimate and is not guaranteed.

Payment can reach an equivalent of $58 per hour, depending on your level and the speed at which you complete tasks. Compensation may vary from one project to another based on scope, complexity, and expertise required.