Data Scientist (Python & SQL) – Freelance AI Trainer

Remote from: Romania
Salary, yearly, USD: 116,000
Department: Data Science & Analytics
Employment type: Full Time,
Job posted: 2 Jun 2026
Apply before: 2 Jul 2026
Experience level: Midweight
Views / Applies: 21 / 7

Mindrift connects AI experts and clients to advance Generative AI models.

Actively Hiring

AI Summary

This freelance AI trainer role at Mindrift involves designing original computational data science problems for leading tech companies. You'll create Python-based problems spanning the full data science pipeline, from data ingestion to deployment considerations. Ideal candidates have 5+ years of hands-on experience, expert Python and SQL skills, and knowledge of modern ML frameworks. The role is project-based with an estimated 10-20 hours per week during active phases.

Role DNA

Job Complexity

Easy Hard

Pace & Pressure

Relaxed Fast-paced

Autonomy Level

Guided Full Ownership

Communication Load

Independent Highly Collaborative

AI Insight The role requires expert-level Python and statistical analysis skills, plus the ability to design complex, deterministic problems. This demands deep expertise and creativity, placing it at a high difficulty level.

Salary Analysis

AI Insight The offered salary of $116,000 per year (equivalent to $58/hour) is competitive for a freelance data science role, falling within the typical US market range of $80,000 to $150,000. However, the lack of benefits and project-based nature should be considered.

Key Skills

Python SQL Data Science Machine Learning Statistical Analysis Pandas Scikit-learn Problem Design AI Training Freelance

Cover Letter Sample

Dear Hiring Manager,

I am excited to apply for the Data Scientist (Python & SQL) - Freelance AI Trainer position at Mindrift. With over 5 years of hands-on data science experience and expert proficiency in Python and SQL, I have successfully delivered impactful business solutions across various industries.

My background includes designing end-to-end data science pipelines and creating reproducible analytical problems, which aligns directly with the responsibilities outlined. I am particularly adept at using libraries such as Pandas, Numpy, Scikit-learn, and Statsmodels to solve complex computational challenges.

I am enthusiastic about the opportunity to contribute to cutting-edge AI projects and to leverage my skills in a flexible, project-based environment. Thank you for considering my application.

Sincerely,
[Your Name]

Possible Interview Questions

Describe a time you designed a complex data science problem from scratch. What steps did you take to ensure it was deterministic and reproducible?

I once designed a customer churn prediction problem for a telecom dataset. I defined a fixed random seed, used deterministic algorithms, and avoided any stochastic elements. I validated the solution by running the code multiple times to ensure identical outputs.

How do you approach creating problems that require non-trivial reasoning chains? Can you give an example?

I focus on business scenarios where multiple steps are needed, like fraud detection. For example, I designed a problem where candidates had to join transaction data with customer profiles, perform feature engineering, train a model, and evaluate using precision-recall, requiring logical sequencing and interpretation.

What is your experience with big data processing scenarios, and how do you incorporate scalability into problem design?

I have worked with large datasets using Spark and Dask. In problem design, I specify that solutions should use efficient vectorized operations or chunking. For example, I created a problem where data exceeded memory, requiring candidates to implement out-of-core processing.

How do you ensure that your problems are based on real business challenges and not just academic exercises?

I draw from my industry experience in finance and e-commerce. For instance, I designed a problem on loan default prediction using real-world features like credit history and income, with a focus on business metrics like ROI and cost of false positives.

Explain how you would verify that a candidate's solution to your problem is correct. What criteria do you use?

I provide a reference solution and define exact output formats. I use automated tests to compare outputs, including intermediate results like cleaned data and model performance. I also check for code efficiency and adherence to constraints like fixed random seeds.

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 original computational data science problems that simulate real-world analytical workflows across industries (telecom, finance, government, e-commerce, healthcare)
Create problems requiring Python programming to solve (using Pandas, Numpy, Scipy, Sklearn, Statsmodels, Matplotlib, Seaborn)
Ensure problems are computationally intensive and cannot be solved manually within reasonable timeframes (days/weeks)
Develop problems requiring non-trivial reasoning chains in data processing, statistical analysis, feature engineering, predictive modeling, and insight extraction
Create deterministic problems with reproducible answers: avoid stochastic elements or require fixed random seeds for exact reproducibility
Base problems on real business challenges: customer analytics, risk assessment, fraud detection, forecasting, optimization, and operational efficiency
Design end-to-end problems spanning the complete data science pipeline (data ingestion → cleaning → EDA → modeling → validation → deployment considerations)
Incorporate big data processing scenarios requiring scalable computational approaches
Verify solutions using Python with standard data science libraries and statistical methods
Document problem statements clearly with realistic business contexts and provide verified correct answers

What we look for
This opportunity is a good fit for Data Science specialists with an experience in python open to part-time, non-permanent projects. Ideally, contributors will have:

5+ years of hands-on data science experience with proven business impact
Portfolio of completed projects and publications showcasing real-world problem-solving
Expert Python programming for data science (pandas, numpy, scipy, scikit-learn, statsmodels)
Expert statistical analysis and machine learning – deep understanding of algorithms, methods, and their practical applications
Expert with SQL and database operations for data manipulation and analysis
Experience with GenAI technologies (LLMs, RAG, prompt engineering, vector databases)
Understanding of MLOps practices and model deployment workflows
Knowledge of modern frameworks (TensorFlow, PyTorch, LangChain)
Strong written English (C1+).

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 $58 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.

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