We are seeking experienced Data Scientists to help train next-generation AI models by designing and solving complex, real-world computational challenges. These tasks will reflect authentic industry problems across sectors like finance, healthcare, telecom, e-commerce, and government services, requiring strong technical skills in Python and SQL.
What You’ll Do
- Develop original data science problems that mirror actual business workflows, from data ingestion through modeling and validation
- Build challenges using core Python libraries including pandas, numpy, scipy, sklearn, statsmodels, matplotlib, and seaborn
- Ensure problems demand computational solutions—too complex to solve manually within days or weeks
- Structure tasks to include multi-step reasoning in data transformation, statistical analysis, feature engineering, and insight generation
- Design deterministic scenarios with reproducible outcomes, using fixed random seeds where needed
- Anchor problems in practical applications such as fraud detection, forecasting, risk assessment, customer analytics, and operational optimization
- Include big data contexts that require efficient, scalable processing techniques
- Validate solutions using standard data science methods and provide clear documentation with verified answers
Requirements
- Current residency in the specified country
- Submit a resume in English
- Clearly indicate your level of English proficiency
Technical Environment
Python, SQL, and standard data science libraries: pandas, numpy, scipy, sklearn, statsmodels, matplotlib, seaborn
Work & Compensation
This is a freelance role with full scheduling flexibility. Work at your own pace from your local country. Hourly equivalent is $21, though actual compensation varies by project scope, complexity, and required expertise. You’ll contribute to AI advancements led by leading technology innovators.
Our Approach
We believe in ethically guiding AI’s evolution through collective expertise. You’ll help ensure these systems are built on realistic, diverse, and responsible data practices. This is an opportunity to influence how AI learns, ensuring it serves broad societal benefit through technically rigorous and thoughtfully designed challenges.
