Data Science, Machine Learning Specialist – Kaggle Expert

Gramian Consulting

. Design challenging data science and machine learning tasks inspired by real-world problems and competitive data science scenarios .

Posted 11/10/2026contractRemote • United States, Canada, Australia, New Zealand, United KingdomMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in competitive data science and machine learning, with a strong foundation in statistical modeling and quantitative problem-solving. Proficient in Python programming and capable of developing, testing, and verifying data science solutions in reproducible environments.

Highest-signal resume keywords

Competitive Data Science ExperienceMachine Learning ExpertisePython Programming AbilityKaggle Profile with Expert RankingStatistical Modeling Knowledge

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningQuantitative ModelingData AnalysisStatistical ModelingAutomated TestingData PreparationProblem-SolvingTechnical DocumentationDebuggingVerification Procedures

Soft Skills

Analytical ReasoningWritten Communication

Tools & Technologies

KaggleMachine Learning LibrariesComputational Environments

Industry Keywords

Data LeakageImplementation ErrorsMethodological ErrorsEvaluation CriteriaTask Specifications

Tech Stack

Tools & technologies

Python

About the role

Key responsibilities & impact

Design challenging data science and machine learning tasks inspired by real-world problems and competitive data science scenarios
Build reproducible execution environments, prepare datasets, configure dependencies, and implement verified reference solutions
Develop automated tests and evaluation criteria distinguishing correct solutions from incomplete, incorrect, or unreliable approaches
Use coding agents to execute assigned tasks, investigate failures, and evaluate problem-solving capabilities
Identify implementation errors, flawed assumptions, data leakage, statistical inconsistencies, and other issues affecting solution correctness
Apply competitive data science techniques and mathematical reasoning to validate solutions
Ensure tasks have clear requirements, reliable expected outputs, and reproducible verification procedures
Refine task difficulty and evaluation methods based on coding-agent performance
Document task specifications, solution methodologies, technical reasoning, and verification steps
Review and improve task quality to ensure scientific and computational accuracy

Requirements

What you’ll need

Demonstrated experience in competitive data science, machine learning, quantitative modeling, or applied mathematics
Kaggle profile with at least Expert ranking; Master or Grandmaster ranking strongly valued
Verifiable Kaggle profile demonstrating competition achievements and ranking
Strong understanding of machine learning, statistical modeling, data analysis, and quantitative problem-solving
Practical programming ability, particularly in Python
Ability to independently implement, test, debug, and verify data science solutions
Experience working with datasets, computational environments, and relevant machine learning libraries
Strong analytical reasoning and ability to explain technical decisions
Ability to identify data leakage, implementation flaws, and methodological errors
Excellent written communication and technical documentation skills
Professional English communication skills

Benefits

Comp & perks

Flexible schedule; choose working days and hours, including weekends
Approximately 15 hours per week