About the job
This opportunity is exclusively available for candidates currently residing in Portugal. Your location may impact eligibility and compensation. Please submit your resume in English and specify your English proficiency level.
At Mindrift, we merge innovation with opportunity. Our mission is to harness the power of collective intelligence to ethically shape the future of artificial intelligence.
About Us
The Mindrift platform connects specialists with groundbreaking AI projects from leading tech innovators. Our goal is to unlock the potential of Generative AI by leveraging real-world expertise from around the globe.
Role Overview
The landscape of Generative AI models is evolving rapidly, and we aim to enhance their ability to tackle specialized inquiries and develop complex reasoning capabilities. As a Data Science AI Trainer on our platform, you will have the opportunity to collaborate on innovative projects.
While each project is distinct, typical responsibilities may include:
- Designing original computational data science challenges that replicate real-world analytical workflows across various sectors (telecommunications, finance, government, e-commerce, healthcare).
- Creating complex problems that necessitate Python programming to resolve (utilizing libraries such as pandas, numpy, scipy, sklearn, statsmodels, matplotlib, seaborn).
- Ensuring that problems are computationally demanding and cannot be solved manually within a reasonable timeframe (days or weeks).
- Developing problems that require intricate reasoning in data processing, statistical analysis, feature engineering, predictive modeling, and insight extraction.
- Formulating deterministic problems with reproducible outcomes: avoiding stochastic factors or mandating fixed random seeds for precise reproducibility.
- Base challenges on genuine business scenarios: customer analytics, risk assessment, fraud detection, forecasting, optimization, and operational efficiency.
- Designing end-to-end problems that encompass the complete data science pipeline (data ingestion → cleaning → exploratory data analysis (EDA) → modeling → validation → deployment considerations).
- Incorporating big data scenarios that require scalable computational methodologies.
- Verifying solutions using Python with standard data science libraries and statistical approaches.
- Clearly documenting problem statements with realistic business contexts and providing verified correct answers.
Getting Started
To apply, simply submit your application for this position. If qualified, you'll have the chance to contribute to projects that align with your skills, on your own schedule. From creating training prompts to refining model responses, you'll play a vital role in shaping the future of AI while ensuring that technology benefits everyone.

