We are sharing a specialised remote consulting opportunity for experienced Data Scientists, Machine Learning Practitioners, and Applied Mathematics Experts with strong backgrounds in competitive data science, quantitative modelling, programming, and mathematical problem-solving to contribute their technical knowledge to advanced AI training and evaluation initiatives.
Selected professionals will develop challenging data science and machine learning tasks, build reproducible computational environments, implement verified reference solutions, and evaluate the performance of AI coding agents. The work combines practical competition experience with algorithmic reasoning, statistical analysis, automated testing, and technical verification to improve the reliability and problem-solving capabilities of advanced AI systems.
Key Responsibilities
Competitive Data Science & Problem Development
Design challenging data science and machine learning tasks informed by real-world analytical problems and competitive programming environments
Apply experience from Kaggle, DrivenData, or comparable competitions to develop technically rigorous assignments
Formulate problems requiring mathematical reasoning, statistical understanding, and practical coding expertise
Identify relevant datasets, modelling objectives, and computational requirements for individual tasks
Develop clear task specifications that reflect realistic data science and machine learning workflows
Reproducible Environments & Reference Solutions
Build reproducible computational environments containing the data, dependencies, and execution requirements necessary for assigned tasks
Implement technically accurate reference solutions using appropriate programming and analytical methods
Prepare datasets and supporting resources to enable consistent task execution and evaluation
Validate reference implementations for correctness, reproducibility, and alignment with task requirements
Document environment configurations, implementation decisions, and relevant computational assumptions
Machine Learning & Quantitative Analysis
Apply machine learning, statistical modelling, and mathematical reasoning to complex analytical challenges
Evaluate modelling strategies, algorithmic approaches, and computational techniques against specified objectives
Identify flawed assumptions, implementation errors, and methodological inconsistencies affecting solution quality
Assess potential data leakage and other issues that compromise the validity of analytical results
Apply appropriate quantitative reasoning to distinguish technically sound solutions from incomplete or incorrect approaches
Coding Agent Evaluation & Automated Testing
Develop automated tests and evaluation criteria that reliably assess solution correctness
Run AI coding agents against assigned tasks and analyse their technical performance
Investigate agent-generated errors, incomplete implementations, and weaknesses in problem-solving approaches
Refine task specifications, reference solutions, and verification procedures based on observed agent behaviour
Ensure evaluation criteria distinguish valid solutions from flawed approaches through reproducible technical checks
Technical Documentation & Quality Assurance
Document task objectives, computational requirements, implementation approaches, and verification procedures
Explain mathematical reasoning, modelling decisions, and technical solutions clearly and accurately
Review code, analytical outputs, and test results for correctness and consistency
Identify opportunities to improve task clarity, computational reliability, and evaluation effectiveness
Maintain high standards of technical accuracy, reproducibility, and structured documentation across assigned deliverables
Ideal Profile
Demonstrated experience in competitive data science, machine learning, quantitative modelling, or applied mathematical problem-solving
Participation in Kaggle, DrivenData, or comparable data science and machine learning competitions
Experience with competition platforms such as Zindi, AIcrowd, Numerai, DataSource.ai, or CodaLab/Codabench is also relevant
Strong understanding of machine learning methodologies, statistical analysis, and mathematical reasoning
Practical programming skills sufficient to independently implement, test, and verify computational solutions
Ability to design technically challenging problems with clearly defined objectives and verifiable outcomes
Experience preparing datasets, configuring computational environments, and managing technical dependencies
Familiarity with reproducible experimentation and systematic validation of analytical results
Strong understanding of common modelling errors, flawed assumptions, and data leakage
Ability to evaluate algorithmic correctness and identify implementation weaknesses
Experience developing automated tests and objective criteria for assessing programming solutions
Strong analytical reasoning, logical thinking, and independent problem-solving capabilities
Advanced Python programming experience, including debugging, testing, and working with unfamiliar codebases, is highly advantageous
Familiarity with machine learning engineering workflows and practical model evaluation techniques
Ability to investigate complex technical problems and explain underlying issues clearly
Strong written communication and technical documentation skills
Experience working independently on computational assignments and delivering reproducible technical outputs
Familiarity with AI coding agents or automated programming evaluation is advantageous
Ability to manage technical assignments efficiently within a flexible remote consulting arrangement
Prior experience with AI model training or evaluation projects is not required
Engagement Details
Freelance / contract engagement
Fully remote — open worldwide
Compensation: $60–$120/hour
Expected Commitment: Approximately 15 hours per week
Flexible Schedule: Professionals may choose their working hours and days, including weekends
50 available openings
Specialised data science consulting role spanning competitive machine learning, applied mathematics, computational problem-solving, and AI evaluation
Competition Experience: Kaggle, DrivenData, Zindi, AIcrowd, Numerai, DataSource.ai, or CodaLab/Codabench
Technical Focus: Machine learning, quantitative modelling, mathematical reasoning, programming, automated testing, and reproducible environments
Work will involve creating challenging analytical tasks, implementing verified reference solutions, and developing reliable evaluation criteria
Assignments may include running AI coding agents, investigating implementation errors, identifying data leakage, and refining technical challenges
Strong programming capabilities, particularly in Python, are advantageous
Professionals will be expected to independently implement solutions and verify their correctness through reproducible technical methods
Clear technical documentation, reliable verification procedures, and analytical accuracy are important for project deliverables
Previous experience with AI training projects is not required
About the Platform
This opportunity is available through 24-MAG LLC. We connect experienced professionals with remote consulting opportunities across technical, evaluation, and project-based workstreams.
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