About the job
This opportunity is exclusively available for candidates currently residing in Sweden. Your location may influence eligibility and compensation. Please submit your resume in English and specify your English proficiency level.
At Mindrift, we are at the forefront of harnessing collective intelligence to ethically shape the future of artificial intelligence.
About Us
The Mindrift platform connects skilled professionals with AI projects led by major tech innovators. Our mission is to unlock the capabilities of Generative AI by leveraging real-world expertise from around the globe.
Position Overview
As a Data Science AI Trainer on our platform, you will play a key role in enhancing GenAI models, making them capable of addressing specialized queries and developing complex reasoning skills. You will have the chance to collaborate on a variety of projects, each with its unique challenges.
In this role, your typical responsibilities may include:
- Designing innovative computational data science problems that reflect real-world analytical workflows in sectors such as telecom, finance, government, e-commerce, and healthcare.
- Creating data science problems that necessitate Python programming skills (utilizing libraries like pandas, numpy, scipy, sklearn, statsmodels, matplotlib, seaborn).
- Ensuring that these problems are computationally intensive, necessitating advanced solutions that cannot be manually resolved within reasonable timeframes.
- Formulating challenges that require intricate reasoning chains across data processing, statistical analysis, feature engineering, predictive modeling, and deriving insights.
- Developing deterministic problems with reproducible outcomes, avoiding stochastic elements unless fixed random seeds are used.
- Grounding problems in tangible business challenges, including customer analytics, risk assessment, fraud detection, forecasting, optimization, and operational efficiency.
- Designing comprehensive problems that cover the entire data science workflow (data ingestion → cleaning → exploratory data analysis → modeling → validation → deployment considerations).
- Incorporating scenarios that demand big data processing and scalable computational techniques.
- Validating solutions using Python and standard data science libraries along with statistical methodologies.
- Clearly documenting problem statements with realistic business contexts and providing verified correct answers.
Getting Started
To embark on this exciting journey, simply apply to this listing, qualify, and seize the opportunity to contribute to projects that align with your expertise, all while working on your own schedule. Your contributions will help refine AI models and ensure technology serves everyone positively.

