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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