Data Science Expert - AI Model Evaluation

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 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:

3+ years of professional experience in data science, statistics, analytics, machine learning, or a related field
Strong statistical, analytical, and quantitative reasoning skills
Experience working with real-world datasets and data-driven problems
Ability to evaluate data science analysis, models, and conclusions critically
Strong attention to detail
Ability to identify errors, inconsistencies, flawed assumptions, and methodological issues
Strong professional judgment
Ability to clearly explain why a data science approach or conclusion is correct or incorrect
Experience with any of the following is valuable:
Statistical analysis
Machine learning
Predictive modeling
Data visualization
Experimental design and A/B testing
Feature engineering
Time series and forecasting
Causal inference
Data analytics
Python, R, SQL, or similar tools
Model evaluation and validation
Data quality and preprocessing
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

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

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

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

Do the work
Complete projects remotely and asynchronously.

Get paid
Receive the agreed rate for your completed work.

Help teach AI how great data scientists actually work.

Apply directly through: alpheva{Dot}ai/experts/