Competitive Data Scientist (Kaggle)
New
W
Weekday AIAI training
India. United StatesContract
Salary60 - 120 USD per hour
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Job Details
- Required Skills
- PythonMachine LearningData modeling
Requirements
- 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 ability to break complex problems into structured, testable solutions.
- Practical programming skills 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 ability to communicate technical reasoning clearly.
- Experience with DrivenData, Zindi, AIcrowd, Numerai, or similar competition platforms is beneficial.
- Experience with pandas, NumPy, scikit-learn, PyTorch, or TensorFlow is beneficial.
- Knowledge of automated testing, reproducible environments, and dependency management is beneficial.
- Familiarity with AI coding agents, LLM-based development tools, and agent evaluation is beneficial.
Responsibilities
- Design challenging data science, machine learning, and mathematical modeling tasks based on real-world problems and competition experience.
- Build reproducible execution environments, prepare datasets, and configure dependencies.
- Implement accurate, efficient, and verifiable reference solutions.
- Develop automated tests, validation criteria, and evaluation frameworks for assessing solution quality.
- Run AI coding agents against tasks, analyze their performance, and investigate failure patterns.
- Refine task specifications based on observed results.
- Identify bugs, incorrect assumptions, data leakage, and methodological weaknesses.
- Document problem statements, expected outputs, solution methods, dependencies, and verification procedures.
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