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 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.
How to get started
Simply apply to this post, qualify, and get the chance to contribute to projects aligned with your skills, on your own schedule. From creating training prompts to refining model responses, you’ll help shape the future of AI while ensuring technology benefits everyone.
Requirements
- You hold a Master’s or PhD Degree in Data Science, Statistics, Mathematics, Computer Science, or related quantitative field.
- You have at least 5 years of hands-on data science experience with proven business impact.
- You have portfolio of completed projects and publications showcasing real-world problem-solving.
- You are proficient in python programming for data science (pandas, numpy, scipy, scikit-learn, statsmodels).
- You are an expert in statistical analysis and machine learning with deep understanding of algorithms, methods, and their practical applications.
- You are proficient in SQL and database operations for data manipulation and analysis.
- You have experience with GenAI technologies (LLMs, RAG, prompt engineering, vector databases).
- You have good understanding of MLOps practices and model deployment workflows.
- You possess knowledge of modern frameworks (TensorFlow, PyTorch, LangChain).
- Your level of English is advanced (C1) or above.
- You are ready to learn new methods, able to switch between tasks and topics quickly and sometimes work with challenging, complex guidelines.
- Our freelance role is fully remote so, you just need a laptop, internet connection, time available and enthusiasm to take on a challenge.
Benefits
Why this freelance opportunity might be a great fit for you?
- Get paid for your expertise, with rates that can go up to $34/hour depending on your skills, experience, and project needs.
- Take part in a part-time, remote, freelance project that fits around your primary professional or academic commitments.
- Work on advanced AI projects and gain valuable experience that enhances your portfolio.
- Influence how future AI models understand and communicate in your field of expertise.