Employer description & source details
Task Development: Design challenging data science, machine learning, and mathematical modeling tasks based on real-world problems and competitive programming experience. Environment Setup: Build reproducible execution environments, prepare datasets, configure dependencies, and ensure consistent task execution. Reference Solutions: Implement accurate, efficient, and verifiable reference solutions for each task. Testing and Evaluation: Develop automated tests, validation criteria, and evaluation frameworks that distinguish correct solutions from incomplete, inefficient, or flawed implementations. AI Agent Evaluation: Run AI coding agents against designed tasks, analyze their performance, investigate failure patterns, and refine task specifications based on observed results. Debugging and Quality Assurance: Identify implementation bugs, incorrect assumptions, data leakage, methodological weaknesses, and other issues that compromise solution quality. Technical Documentation: Clearly document problem statements, expected outputs, solution methodologies, dependencies, and verification procedures. Continuous Improvement: Improve task complexity, reproducibility, and evaluation reliability through iterative testing and experimentation. Demonstrated experience in data science, machine learning, quantitative modeling, applied mathematics, or a related technical field. Participation in competitive data science or machine learning platforms, such as Kaggle, DrivenData, Zindi, AIcrowd, Numerai, DataSource.ai, or CodaLab/Codabench. Strong analytical reasoning and the ability to break down complex problems into structured, testable solutions. Practical programming skills sufficient to independently implement, test, debug, and validate technical solutions. Understanding of statistical
Extracted skills: python, machine learning