About the job
We invite you to submit your CV in English and specify your English proficiency level.
Mindrift connects talented professionals with project-based AI opportunities at leading technology firms, specializing in the testing, evaluation, and enhancement of AI systems. Please note that participation is project-based and does not constitute permanent employment.
Key Responsibilities
While each project presents distinct challenges, contributions may include:
- Designing innovative computational data science tasks that emulate real-world analytical workflows across various sectors including telecom, finance, government, e-commerce, and healthcare.
- Developing tasks that require Python programming solutions (utilizing libraries such as Pandas, Numpy, Scipy, Sklearn, Statsmodels, Matplotlib, and Seaborn).
- Ensuring tasks are computationally demanding and cannot be addressed manually within reasonable timeframes (days or weeks).
- Creating problems that necessitate complex reasoning chains in data processing, statistical analysis, feature engineering, predictive modeling, and extracting insights.
- Designing deterministic problems with reproducible outcomes: avoiding stochastic elements or requiring fixed random seeds for precise reproducibility.
- Grounding tasks in real business scenarios: including customer analytics, risk assessment, fraud detection, forecasting, optimization, and enhancing operational efficiency.
- Crafting end-to-end problems that encompass the entire data science pipeline (data ingestion → cleaning → exploratory data analysis → modeling → validation → deployment considerations).
- Incorporating big data scenarios that demand scalable computational strategies.
- Validating solutions using Python alongside standard data science libraries and statistical methodologies.
- Documenting problem statements clearly with relevant business contexts and providing verified correct answers.
Qualifications
This role is ideal for Data Science specialists with experience in Python who are open to part-time, non-permanent projects. Preferred candidates will possess:
- Over 5 years of practical data science experience demonstrating a measurable business impact.
- A portfolio showcasing completed projects and publications that highlight real-world problem-solving capabilities.
- Expertise in Python programming for data science (including libraries such as pandas, numpy, scipy, scikit-learn, and statsmodels).
- Advanced knowledge in statistical analysis and machine learning, with a deep understanding of algorithms, methods, and their applications.
- Proficiency in SQL and database operations for data analysis and manipulation.
- Familiarity with GenAI technologies (LLMs, RAG, prompt engineering, vector databases).
- Understanding of MLOps practices and workflows for model deployment.
- Knowledge of contemporary frameworks (TensorFlow, PyTorch, LangChain).
- Strong written English skills (C1+ level).
Application Process
Apply → Pass qualifications → Join a project → Complete tasks → Receive payment
