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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
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+).
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