About the job
This opportunity is exclusively available to candidates residing in Jordan. Your location may influence eligibility and compensation rates. Please submit your resume in English and include your proficiency level.
At Mindrift, we merge innovation with opportunity. We harness the power of collective intelligence to ethically shape the future of AI.
About Us
The Mindrift platform connects specialists with AI projects from leading tech innovators. Our mission is to unlock the potential of Generative AI by leveraging real-world expertise from around the world.
Role Overview
As a Data Science AI Trainer, you will play a crucial role in advancing GenAI models, which are rapidly evolving to tackle specialized inquiries and develop complex reasoning abilities. This position allows you to collaborate on innovative projects that contribute to the advancement of AI technology.
Typical responsibilities may include:
- Crafting original computational data science challenges that emulate real-world analytical processes across various sectors (such as telecom, finance, government, e-commerce, and healthcare).
- Developing problems that necessitate Python programming for resolution (utilizing libraries such as pandas, numpy, scipy, sklearn, statsmodels, matplotlib, seaborn).
- Creating computationally intensive challenges that cannot be solved manually within a reasonable timeframe (days/weeks).
- Designing problems that require complex reasoning in areas such as data processing, statistical analysis, feature engineering, predictive modeling, and insight extraction.
- Ensuring problems yield deterministic outcomes with reproducible answers, avoiding stochastic elements or implementing fixed random seeds for exact reproducibility.
- Focusing on real business challenges: customer analytics, risk assessment, fraud detection, forecasting, optimization, and operational efficiency.
- Formulating end-to-end problems that encompass the entire data science pipeline (data ingestion → cleaning → exploratory data analysis → modeling → validation → deployment considerations).
- Incorporating big data processing scenarios that require scalable computational methods.
- Validating solutions with Python, employing standard data science libraries and statistical techniques.
- Documenting problem statements clearly with realistic business contexts and providing verified correct answers.
How to Apply
To get started, simply apply to this post, meet the qualifications, and seize the opportunity to engage in projects that align with your expertise, on your own terms. From crafting training prompts to fine-tuning model responses, you will contribute to shaping the future of AI while ensuring that technology serves everyone.

