Freelance Data Science Engineer
Pubblicato il 08-09-2026 - Mindrift in Turbigo
This opportunity is only for candidates currently residing in the specified country. Your location may affect eligibility and rates. Please submit your resume in English and indicate your level of English proficiency.
At Mindrift, innovation meets opportunity. We believe in using the power of collective intelligence to ethically shape the future of AI.
What We Do
The Mindrift platform connects specialists with AI projects from major tech innovators. Our mission is to unlock the potential of Generative AI by tapping into real-world expertise from across the globe.
About The Role
GenAI models are improving very quickly, and one of our goals is to make them capable of addressing specialized questions and achieving complex reasoning skills. If you join the platform as a Data Science AI Trainer, you'll have the opportunity to collaborate on these projects.
Although every project is unique, you might typically:
- 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 scenario