Freelance Data Science Engineer (Python & SQL)

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.

• 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 with deep understanding of algorithms, methods, and practical applications
• Expert with SQL and database operations for data manipulation and analysis
• Experience with GenAI technologies including LLMs, RAG, prompt engineering, vector databases
• Understanding of MLOps practices and model deployment workflows
• Knowledge of modern frameworks such as TensorFlow, PyTorch, LangChain
• Strong written English proficiency (C1+)

• Design original computational data science problems simulating real-world analytical workflows across industries such as telecom, finance, government, e-commerce, and healthcare
• Create problems requiring Python programming using libraries like Pandas, Numpy, Scipy, Sklearn, Statsmodels, Matplotlib, Seaborn
• Ensure problems are computationally intensive and cannot be solved manually within reasonable timeframes
• 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, avoiding stochastic elements or requiring fixed random seeds
• Base problems on real business challenges including customer analytics, risk assessment, fraud detection, forecasting, optimization, and operational efficiency
• Design end-to-end problems spanning the complete data science pipeline from data ingestion to 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

Participation is project-based, not permanent employment.