Competitive Data Scientist (Kaggle)

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. Requirements Key Responsibilities Task Development: Design challenging data science, machine learning, and mathematical modeling tasks based on real-world problems and competitive programming experienceEnvironment Setup: Build reproducible execution environments, prepare datasets, configure dependencies, and ensure consistent task executionReference Solutions: Implement accurate, efficient, and verifiable reference solutions for each taskTesting and Evaluation: Develop automated tests, validation criteria, and evaluation frameworks that distinguish correct solutions from incomplete, inefficient, or flawed implementationsAI Agent Evaluation: Run AI coding agents against designed tasks, analyze their performance, investigate failure patterns, and refine task specifications based on observed resultsDebugging and Quality Assurance: Identify implementation bugs, incorrect assumptions, data leakage, methodological weaknesses, and other issues that compromise solution qualityTechnical Documentation: Clearly document problem statements, expected outputs, solution methodologies, dependencies, and verification proceduresContinuous 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 fieldParticipation in competitive data science or machine learning platforms, such as Kaggle, DrivenData, Zindi, AIcrowd, Numerai, DataSource.ai, or CodaLab/CodabenchStrong analytical reasoning and the ability to break down complex problems into structured, testable solutionsPractical programming skills sufficient to independently implement, test, debug, and validate technical solutionsUnderstanding of statistical methods, predictive modeling, data preprocessing, feature engineering, and model evaluationAbility to identify data leakage, overfitting, flawed assumptions, and methodological errorsExperience evaluating model performance and validating results against reliable benchmarksStrong attention to detail and the ability to communicate technical reasoning clearly Must-Have Skills Competitive Data ScienceKaggle CompetitionsMachine LearningPython ProgrammingStatistical Analysis and Mathematical ReasoningData Modeling and Model EvaluationTesting, Debugging, and Solution Validation Good-to-Have Skills Experience with DrivenData, Zindi, AIcrowd, Numerai, or similar competition platformsStrong Python development skills, including working with unfamiliar codebasesExperience with pandas, NumPy, scikit-learn, PyTorch, or TensorFlowKnowledge of automated testing, reproducible environments, and dependency managementExperience developing evaluation benchmarks and automated grading systemsFamiliarity with AI coding agents, LLM-based development tools, and agent evaluationExperience 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.