Freelance Data Science Engineer (Python & SQL)

Mindrift

About The Position

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. This opportunity involves designing original computational data science problems that simulate real-world analytical workflows across various industries. Contributors will create problems requiring Python programming (using Pandas, Numpy, Scipy, Sklearn, Statsmodels, Matplotlib, Seaborn), ensuring they are computationally intensive and require non-trivial reasoning chains in data processing, statistical analysis, feature engineering, predictive modeling, and insight extraction. Problems must be deterministic with reproducible answers, based on real business challenges, and span the complete data science pipeline from data ingestion to deployment considerations, potentially incorporating big data processing scenarios. Solutions will be verified using Python with standard data science libraries and statistical methods, and problem statements must be clearly documented with realistic business contexts and verified correct answers.

Requirements

Experience in python open to part-time, non-permanent projects
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+)

Responsibilities

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