ജോലി വിവരണം

Overview

Mindrift brings together subject-matter experts and project-based AI work for major technology companies. The focus is on evaluating, testing, and refining AI systems. This is a project-based arrangement rather than a permanent role.

Applicants are asked to submit a CV in English and clearly state their English proficiency level.

What you may do in this project

Develop original data science challenges that mirror practical analytics workflows across sectors such as telecom, finance, public services, online retail, and healthcare.
Build tasks that require Python-based problem solving using libraries such as Pandas, NumPy, SciPy, scikit-learn, Statsmodels, Matplotlib, and Seaborn.
Make sure the tasks are computationally demanding and would not be reasonably solvable by hand within a short period; the expected solving horizon is days or weeks.
Design questions that require layered reasoning in data preparation, statistical analysis, feature engineering, predictive modeling, and interpretation of results.
Create deterministic exercises with reproducible outputs by avoiding randomness or by using fixed seeds where needed.
Ground the problems in realistic business situations such as customer analytics, risk review, fraud detection, forecasting, optimization, and efficiency improvement.
Cover the full data science workflow from data ingestion and cleaning through exploratory analysis, modeling, validation, and deployment considerations.
Include large-scale data processing scenarios that call for efficient and scalable methods.
Check solutions with Python-based data science tools and statistical techniques.
Write clear problem statements with practical business context and verified correct answers.

Profile sought

This opportunity suits 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 measurable business results.
A track record of completed projects and publications demonstrating applied problem solving.
Advanced Python skills for data science work, including pandas, numpy, scipy, scikit-learn, and statsmodels.
Strong command of statistical analysis and machine learning, including understanding of algorithms, methods, and where they apply best.
Advanced SQL knowledge and comfort working with databases for data manipulation and analysis.
Exposure to GenAI tools and workflows, including LLMs, RAG, prompt engineering, and vector databases.
Familiarity with MLOps practices 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 work process runs

The typical flow is: application, qualification steps, project assignment, task completion, and payment.

Time commitment

During active phases, 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 active.

Compensation

Contributors on this project can earn up to the equivalent of $58 per hour, depending on expertise and contribution pace.

Pay differs from project to project based on scope, complexity, and skill requirements, so other assignments may have different earning levels.

Additional information

Participation is not permanent employment; it is tied to individual projects.

Data Scientist (Python & SQL) - Freelance AI Trainer