Overview

Get paid to help build better AI.Alpheva AI, the Silicon Valley company that is building intelligence behind frontier AI, is looking for experienced data science professionals to participate in paid, project-based work that helps train and evaluate the next generation of AI systems.You work remotely and asynchronously on projects that fit around your existing schedule.Your data science expertise becomes the signal that helps AI systems learn what accurate, rigorous, and professionally sound data science work actually looks like.IMPORTANT: To be considered, Apply directly through: alpheva Dot ai/experts/What you'll doDepending on the project, you may:Analyze: Review datasets, statistical analyses, experiments, models, metrics, and data-driven conclusions.Evaluate: Review AI-generated data science solutions, analyses, code, models, explanations, and reasoning.Validate: Determine whether AI-generated outputs are statistically sound, technically accurate, logically consistent, and supported by the available data.Review: Assess data pipelines, feature engineering, model selection, statistical methods, experiments, visualizations, and machine learning approaches.Reason: Solve realistic data science problems and explain how an experienced data scientist would approach them.Compare solutions: Assess multiple AI-generated data science solutions and identify meaningful differences in accuracy, methodology, reasoning, and professional judgment.Create evaluations: Develop challenging data science tasks and criteria for measuring AI systems.Provide expert judgment: Identify errors, flawed assumptions, data quality issues, methodological weaknesses, and unsupported conclusions in AI-generated work.Projects may involve statistical analysis, machine learning, predictive modeling, experimentation, data visualization, feature engineering, forecasting, causal inference, data analytics, and other areas depending on your expertise.Who we're looking forWe're looking for experienced data science professionals, not necessarily AI researchers.You should have:3+ years of professional experience in data science, statistics, analytics, machine learning, or a related fieldStrong statistical, analytical, and quantitative reasoning skillsExperience working with real-world datasets and data-driven problemsAbility to evaluate data science analysis, models, and conclusions criticallyStrong attention to detailAbility to identify errors, inconsistencies, flawed assumptions, and methodological issuesStrong professional judgmentAbility to clearly explain why a data science approach or conclusion is correct or incorrectExperience with any of the following is valuable:Statistical analysisMachine learningPredictive modelingData visualizationExperimental design and A/B testingFeature engineeringTime series and forecastingCausal inferenceData analyticsPython, R, SQL, or similar toolsModel evaluation and validationData quality and preprocessingYou do not need prior experience working in AI.What matters most is that you know how to do data science work well.You might be asked to:Review an AI-generated data science analysis and identify the issues.Evaluate whether a statistical analysis is technically correct and supported by the data.Assess whether a machine learning approach is appropriate for a given problem.Compare two AI-generated data science solutions and determine which is stronger.Review an AI-generated model evaluation and identify methodological weaknesses.Analyze a realistic dataset or data science scenario and explain your reasoning.Identify data quality issues, statistical errors, flawed assumptions, or unsupported conclusions.Evaluate whether an AI-generated visualization or interpretation accurately represents the underlying data.Create challenging data science problems that can be used to evaluate AI systems.The goal is not simply to determine whether data science work looks reasonable.The goal is to capture the judgment an experienced data scientist uses