About the job
We invite you to submit your CV in English, along with your English proficiency level.
At Mindrift, we bridge the gap between skilled professionals and project-based AI opportunities with leading technology firms. Our focus is on testing, evaluating, and enhancing AI systems. Note: Participation is project-based and does not lead to permanent employment.
Opportunity Overview:
Each project presents its own unique challenges, and contributors may be responsible for:
- Creating original computational data science problems that mirror real-world analytical workflows across diverse sectors including telecom, finance, government, e-commerce, and healthcare.
- Developing problems that necessitate Python programming for solutions (utilizing libraries such as Pandas, Numpy, Scipy, Sklearn, Statsmodels, Matplotlib, Seaborn).
- Ensuring that problems are computationally intensive, thus requiring several days or weeks to solve manually.
- Designing problems that involve complex reasoning pathways in data processing, statistical analysis, feature engineering, predictive modeling, and insight extraction.
- Formulating deterministic problems with reproducible outcomes, avoiding stochastic elements unless fixed random seeds are employed.
- Basing problems on actual business challenges such as customer analytics, risk assessment, fraud detection, forecasting, optimization, and operational efficiency.
- Crafting comprehensive problems that cover the entire data science pipeline (data ingestion → cleaning → exploratory data analysis → modeling → validation → deployment considerations).
- Integrating big data processing scenarios that demand scalable computational strategies.
- Validating solutions using Python and standard data science libraries alongside statistical methods.
- Clearly documenting problem statements within realistic business contexts and providing verified correct answers.
Qualifications:
This opportunity is ideal for Data Science specialists with Python expertise who are open to part-time, non-permanent projects. Preferred qualifications include:
- 5+ years of practical data science experience demonstrating tangible business impact.
- A portfolio of completed projects and publications that showcase real-world problem-solving capabilities.
- Proficiency in Python programming for data science (including pandas, numpy, scipy, scikit-learn, statsmodels).
- Advanced statistical analysis and machine learning expertise, with a deep understanding of algorithms, methodologies, and their practical applications.
- Strong SQL skills and experience in database operations for data manipulation and analysis.
- Familiarity with GenAI technologies (LLMs, RAG, prompt engineering, vector databases).
- Understanding of MLOps practices and model deployment workflows.
- Knowledge of modern frameworks (TensorFlow, PyTorch, LangChain).
- Excellent written English proficiency (C1+).
Process Overview:
Application → Qualification Process → Project Engagement → Task Completion → Payment.
