About the job
This opportunity is exclusively for candidates currently based in Romania. Your location may influence eligibility and compensation. Please submit your resume in English, along with your English proficiency level.
At Mindrift, we merge innovation with opportunity. Our mission is to leverage collective intelligence to ethically shape the future of artificial intelligence.
What We Do
The Mindrift platform facilitates connections between experts and AI initiatives from leading technology innovators. Our goal is to harness the potential of Generative AI by integrating real-world expertise from around the world.
Role Overview
Generative AI models are rapidly evolving, and one of our objectives is to enhance their ability to tackle specialized queries and perform sophisticated reasoning. As a Data Science AI Trainer on our platform, you will have the chance to collaborate on these exciting projects.
While each project is distinct, your typical responsibilities may include:
- Crafting unique computational data science challenges that mimic real-world analytical processes across various sectors (telecom, finance, government, e-commerce, healthcare).
- Designing tasks that necessitate Python programming for solution development (utilizing libraries such as pandas, numpy, scipy, sklearn, statsmodels, matplotlib, seaborn).
- Ensuring that tasks are computationally demanding and cannot be resolved manually within reasonable timeframes (days or weeks).
- Creating problems that involve intricate reasoning chains in areas such as data processing, statistical analysis, feature engineering, predictive modeling, and insight generation.
- Developing deterministic challenges with reproducible outcomes: avoiding stochastic elements or establishing fixed random seeds for exact replicability.
- Formulating tasks based on genuine business problems: customer analytics, risk evaluation, fraud detection, forecasting, optimization, and enhancing operational efficiency.
- Designing comprehensive problems that encompass the entire data science workflow (data ingestion → cleaning → exploratory data analysis → modeling → validation → deployment considerations).
- Incorporating big data processing scenarios that require scalable computational methodologies.
- Validating solutions using Python with standard data science libraries and statistical techniques.
- Clearly documenting problem statements with realistic business contexts and providing verified accurate answers.
How to Apply
Simply submit your application, qualify, and seize the opportunity to contribute to projects that resonate with your skills, on your own schedule. From generating training prompts to refining model outputs, you will play a pivotal role in shaping the future of AI and ensuring that technology serves everyone's interests.

