This role is for one of our clients Pay: 60–120/hour Location: Global, fully remote Job Type: Contractor (~15 hours per week) Work Schedule: Flexible — choose your preferred working hours and days, including weekends. We are looking for a Competitive Data Scientist (Kaggle) with strong expertise in data science, machine learning, statistical modeling, and applied mathematics. The ideal candidate will have demonstrated experience participating in data science and machine learning competitions, such as Kaggle, DrivenData, or similar platforms. In this role, you will use your competition experience, analytical thinking, and programming skills to design challenging, well-defined coding and data science tasks that require rigorous reasoning rather than plausible or superficial answers. You will build reproducible environments, develop verified reference solutions, and create robust evaluation mechanisms to assess the correctness and quality of AI-generated solutions. This opportunity is ideal for individuals who enjoy solving complex technical problems, experimenting with different approaches, debugging code, and evaluating the performance of AI coding systems.

Key Responsibilities

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.

Required Qualifications

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 methods, predictive modeling, data preprocessing, feature engineering, and model evaluation. Ability to identify data leakage, overfitting, flawed assumptions, and methodological errors. Experience evaluating model performance and validating results against reliable benchmarks. Strong attention to detail and the ability to communicate technical reasoning clearly. Must-Have Skills Competitive Data Science Kaggle Competitions Machine Learning Python Programming Statistical Analysis and Mathematical Reasoning Data Modeling and Model Evaluation Testing, Debugging, and Solution Validation Good-to-Have Skills Experience with DrivenData, Zindi, AIcrowd, Numerai, or similar competition platforms. Strong Python development skills, including working with unfamiliar codebases. Experience with pandas, NumPy, scikit-learn, PyTorch, or TensorFlow. Knowledge of automated testing, reproducible environments, and dependency management. Experience developing evaluation benchmarks and automated grading systems. Familiarity with AI coding agents, LLM-based development tools, and agent evaluation. Experience in quantitative research, optimization, feature engineering, and advanced statistical modeling. Work Arrangement This role offers a flexible schedule, allowing you to choose when and how many days you work, including weekends if preferred. You will have the opportunity to apply your competition experience to challenging technical problems while contributing to the evaluation and improvement of AI systems.