Data Science Expert - AI Model Evaluation

Get paid to help build better AI.

Alpheva AI (www.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.

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What you'll do

Depending 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 for

We're looking for experienced data science professionals, not necessarily AI researchers.

You should have:

1. 3+ years of professional experience in data science, statistics, analytics, machine learning, or a related field
2. Strong statistical, analytical, and quantitative reasoning skills
3. Experience working with real-world datasets and data-driven problems
4. Ability to evaluate data science analysis, models, and conclusions critically
5. Strong attention to detail
6. Ability to identify errors, inconsistencies, flawed assumptions, and methodological issues
7. Strong professional judgment
8. Ability to clearly explain why a data science approach or conclusion is correct or incorrect
9. Experience with any of the following is valuable:
10. Statistical analysis
11. Machine learning
12. Predictive modeling
13. Data visualization
14. Experimental design and A/B testing
15. Feature engineering
16. Time series and forecasting
17. Causal inference
18. Data analytics
19. Python, R, SQL, or similar tools
20. Model evaluation and validation
21. Data quality and preprocessing
22. You 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 to determine whether the analysis actually makes sense.

Compensation

Paid project-based work.

Rates vary based on your experience, specialization, and project requirements. Your applicable rate and project scope will be shown before you commit to a project.

Some projects are short assignments. Others may involve recurring work over a longer period.

There is no fixed schedule and no minimum commitment.

Work arrangement

• Remote
• Asynchronous
• Project-based
• Flexible hours
• Work around your existing job or commitments
• Paid for qualifying project work

But producing a plausible-looking data science answer is not the same as demonstrating strong data science judgment.

AI needs to learn how experienced data scientists:

• Analyze and interpret data
• Select appropriate statistical methods
• Evaluate model performance
• Identify bias and data quality issues
• Design and evaluate experiments
• Assess assumptions and limitations
• Interpret statistical results correctly
• Compare modeling approaches
• Recognize important edge cases
• Identify errors and inconsistencies
• Explain analytical conclusions clearly
• Determine when a data science analysis is actually reliable

That's where you come in.

Your data science expertise helps shape how AI learns to reason about data.

How it works

1. Apply

Tell us about your data science background, experience, qualifications, and areas of expertise.

1. Get qualified

Complete a short assessment designed around the type of data science work you'll perform.

1. Get matched

When your experience matches an active project, we'll share the scope, requirements, and compensation.

1. Do the work

Complete projects remotely and asynchronously.

1. Get paid

Receive the agreed rate for your completed work.

Help teach AI how great data scientists actually work.

Apply directly through: https://alpheva.ai/experts/