We are sharing a specialised part-time consulting opportunity for experienced data science professionals skilled in experiment design, A/B testing, product analytics, metric development, SQL and Python analysis, dashboard review, and structured evaluation of real-world data science work.
This role supports current and upcoming remote consulting opportunities focused on evaluating professional data science deliverables and defining clear standards for high-quality analytical work. Selected professionals will develop task-specific assessment criteria, review AI-generated and human-created outputs, score completed work, and provide detailed written feedback grounded in practical industry experience.
Key Responsibilities
Data Science Deliverable Evaluation
- Review analytical studies, predictive models, dashboards, experiment readouts, and written recommendations
- Assess work for technical accuracy, methodological quality, business relevance, and clarity
- Identify analytical errors, unsupported conclusions, weak assumptions, and inappropriate methodological choices
- Determine whether deliverables meet professional standards for product, growth, and business decision-making
Evaluation Criteria & Rubric Development
- Design precise, task-specific grading criteria for real-world data science deliverables
- Define measurable standards covering analytical approach, statistical validity, metric selection, interpretation, and communication
- Ensure evaluation frameworks reflect realistic expectations from industry data science teams
- Refine assessment criteria based on structured feedback and calibration outcomes
Experimentation & Product Analytics Review
- Evaluate experiment design, A/B testing methodology, sample construction, statistical analysis, and result interpretation
- Review product, growth, and operational metrics for relevance, consistency, and decision-making value
- Assess whether findings are supported by appropriate evidence and analytical methods
- Identify risks involving bias, confounding variables, metric selection, or overinterpretation
SQL, Python & Analytical Review
- Review SQL and Python-based analyses for correctness, efficiency, reproducibility, and clarity
- Assess data preparation, transformation, validation, and modelling workflows
- Evaluate dashboards and reporting outputs for accuracy, usability, and alignment with business questions
- Review written recommendations for logical consistency and practical relevance
Scoring & Written Justification
- Score AI-generated and human-created work against structured evaluation criteria
- Provide detailed written explanations supporting each assessment decision
- Apply consistent, evidence-based judgment across different datasets, industries, and analytical scenarios
- Incorporate feedback from senior reviewers and adjust evaluation approaches efficiently
Ideal Profile
Strong candidates may have:
- At least 5 years of professional data science experience in an industry environment
- Experience in product, growth, business operations, marketplace, or commercial data science
- Strong command of experiment design, A/B testing, metric development, and statistical analysis
- Advanced practical experience with SQL and Python
- Ability to evaluate models, dashboards, analytical outputs, and written recommendations
- Experience communicating findings to executive, product, operational, or commercial stakeholders
- Exceptional written communication and the ability to explain technical judgments clearly
- Strong attention to detail and comfort having evaluation decisions reviewed and calibrated
Educational Background
- A degree in data science, statistics, computer science, mathematics, economics, engineering, or a related quantitative field is helpful
- Graduate-level training in statistics, econometrics, machine learning, operations research, or a related discipline may be valuable
- Professional experience in product analytics, experimentation, growth analytics, or business intelligence is highly relevant
- Equivalent hands-on experience delivering complex analytical work in industry may also be considered
Nice to Have
- Experience at a leading technology company, digital platform, marketplace, or data-driven consumer business
- Familiarity with causal inference, experimentation platforms, forecasting, or predictive modelling
- Experience developing executive dashboards, metric frameworks, and decision-support materials
- Strong understanding of product funnels, retention, engagement, conversion, and growth metrics
- Experience reviewing work produced by analysts, data scientists, or cross-functional technical teams
- Previous involvement in AI evaluation, structured review, benchmarking, or quality-assurance projects
- Experience developing analytical standards, training materials, or internal review frameworks
Why This Opportunity
- Apply senior-level data science judgment to structured remote evaluation work
- Define what strong analytical reasoning and industry-quality execution should look like
- Review realistic analyses, experiments, models, dashboards, and recommendations
- Use your expertise to improve consistency, accuracy, and rigor across data science evaluation workflows
- Participate in flexible assignments with competitive hourly compensation
Contract Details
- Independent contractor role
- Fully remote with flexible scheduling
- Part-time workload depending on project availability and scope
- Competitive rates between $115–$165 per hour depending on experience, technical depth, and project requirements
- Weekly payments via Stripe or Wise
- The selection process may include a resume review, brief technical assessment, or request for additional professional information
- Projects may be extended, shortened, or adjusted depending on scope and performance
- Work will not involve access to confidential or proprietary information from any employer, client, or institution
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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