Data Scientist

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 What We Look For 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 Expertise 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+) How It Works Apply -> Pass qualification(s) -> Join a project -> Complete tasks -> Get paid Project Time Expectations For this project, tasks are estimated to require around 10-20 hours per week during active phases, based on project requirements. This estimate is not a guaranteed workload and applies only while the project is active. Compensation On this project, contributors can earn up to $34 per hour equivalent , depending on their level and pace of contribution. Compensation varies across projects depending on scope, complexity, and required expertise. Other projects on the platform may offer different earning levels based on their requirements.

Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems. This is a project-based role, not a 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