Senior Data Science AI Task Designer

You will design complex data science problems that reflect full, real-world workflows across fields such as telecom, finance, government, e-commerce, and healthcare. Your work will help evaluate and train advanced AI systems on practical data science tasks.

Each problem must require substantial reasoning and computation, rather than being solvable manually in a reasonable amount of time. You will build and check solutions, consider how they could scale to large datasets, and document the business context, problem, and verified answer.

• Create deterministic problems covering data processing, statistical analysis, feature engineering, predictive modeling, and insight extraction.
• Implement and verify solutions in Python using standard data science libraries.
• Include big-data and scalability considerations where relevant.
• Write clear business contexts, problem statements, technical explanations, and correct answers.
• Design end-to-end tasks that represent realistic data science work across multiple industries.

This is a part-time contractor role for less than 20 hours per week. The work is worldwide, and advanced English is required for precise technical documentation.

• Pay: $50 per hour in USD.
• Hours: Less than 20 hours per week.
• Work setup: Remote and worldwide.
• Language: Advanced English at C1 level or higher.
• Education: A master's degree or PhD in data science, statistics, mathematics, computer science, or a related field.
• Experience: At least 5 years of hands-on data science experience with demonstrable business impact.
• Python: Expert knowledge for data science, including pandas, NumPy, SciPy, scikit-learn, and statsmodels.
• SQL: Proficiency with SQL and database operations.
• Core knowledge: Strong understanding of statistics, machine learning algorithms, and their real-world use.
• Communication: Ability to write clear, precise technical documentation.
• Valued experience: Generative AI, including large language models, retrieval-augmented generation, prompt engineering, and vector databases; MLOps; and frameworks such as TensorFlow, PyTorch, and LangChain.

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