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. 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 What we look for 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:
Requirements
- 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
- Strong written English (C1+).
Nice To Haves
- 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)
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
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What This Job Offers
Job Type
Part-time
Career Level
Mid Level
Education Level
No Education Listed
