We are sharing a specialised opportunity for experienced data scientists interested in joining a talent network for potential future consulting projects involving advanced AI research and real-world data science evaluation. There is currently no immediate project opening associated with this network. Qualified professionals may be contacted as relevant opportunities become available. Future projects may focus on evaluating how effectively AI systems perform practical data science work across statistical modelling, machine learning, experimentation, exploratory analysis, feature engineering, and technical reporting. Key Responsibilities Data Science Evaluation • Evaluate realistic data science deliverables produced by AI systems or human contributors • Review exploratory data analyses, statistical models, machine learning pipelines, and technical reports • Assess analytical correctness, methodology, completeness, and practical usefulness • Identify flawed assumptions, methodological weaknesses, or unsupported conclusions • Apply professional data science judgment to complex analytical work Evaluation Criteria & Rubric Development • Develop precise, task-specific grading criteria for data science assignments • Define standards for high-quality analytical and machine learning work • Create evaluation criteria that distinguish rigorous analysis from incomplete or superficial approaches • Apply consistent standards across multiple work samples • Help ensure assessments are reproducible and defensible Statistical & Machine Learning Review • Review statistical modelling and machine learning workflows • Evaluate feature engineering, model selection, validation, and interpretation • Assess experimentation and A/B testing analyses • Review causal inference approaches where relevant • Examine whether analytical conclusions are appropriately supported by available data Technical Analysis & Written Feedback • Review Python, SQL, notebooks, technical reports, and related analytical outputs • Provide detailed written justification for evaluation scores • Communicate technical findings clearly to both specialist and broader audiences • Incorporate structured reviewer feedback and calibrate evaluations against established standards • Maintain accuracy and consistency across complex review assignments Ideal Profile Strong candidates may have: • At least 1 year of professional data science experience • Strong proficiency in Python and SQL • Hands-on experience with statistical modelling and machine learning • Experience designing or analysing experiments and A/B tests • Familiarity with causal inference methods • Ability to work effectively with messy, real-world datasets • Strong analytical reasoning and attention to detail • Excellent written communication skills • Ability to explain technical conclusions clearly and precisely • Comfort receiving feedback and calibrating professional judgment against established standards Educational Background • A degree in data science, statistics, computer science, mathematics, economics, engineering, or a related quantitative discipline may be highly relevant • Advanced quantitative or machine learning training may strengthen an application • Equivalent professional experience demonstrating strong data science expertise may also be considered • Practical experience delivering rigorous real-world analysis is particularly valuable Nice to Have • Experience at a leading technology, AI research, quantitative finance, or research organisation • Advanced experience with statistical experimentation • Strong causal inference knowledge • Experience building production machine learning workflows • Familiarity with exploratory analysis across large or complex datasets • Experience authoring technical notebooks or analytical reports • Background reviewing or mentoring other data scientists • Prior experience with AI evaluation, structured review, benchmarking, or human-data projects Why This Opportunity • Join a network considered for future advanced AI and data science projects • Apply professional data science expertise to realistic analytical evaluation work • Work across machine learning, statistics, experimentation, causal inference, and exploratory analysis • Help establish rigorous quality standards for AI-generated data science work • Access potential future remote consulting opportunities as relevant projects become available Contract Details • Remote, project-based opportunities • There is no immediate project opening associated with this talent network • Qualified applicants may be contacted as suitable projects become available • Potential rates are expected to range between $95–$145 per hour depending on expertise and individual project scope • Specific workload, duration, schedule, and responsibilities will vary by project • Future work may include data science evaluation, rubric development, analytical review, scoring, and written technical feedback • Joining the network does not guarantee placement on a future project About the Platform This opportunity is available through 24-MAG LLC. 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Remote Skills: A/B Testing, Analysis Skills, Analytical Development, Artificial Intelligence (AI), Benchmarking, Communication Skills, Computer Networks, Computer Science, Consulting, Data Analysis, Data Science, Data Sets, Detail Oriented, Economics, Experiment Design, Financial Mathematics, Machine Learning, Mathematics, Mentoring, Model Review, Model Validation, Production Machining, Project Evaluation, Python Programming/Scripting Language, Quality Metrics, Remote Access, SQL (Structured Query Language), Standards Development, Statistical Modeling, Statistics, Technical Analysis, Technical Consulting, Technical Research, Writing Skills undefined: 24-Mag