Data Science Expert - AI Model Evaluation

Get paid to help build better AI.

Alpheva AI, a Silicon Valley AI company building intelligence infrastructure for frontier AI, is looking for experienced data scientists and analytics professionals to participate in paid, project-based work that helps train and evaluate the next generation of AI systems.

You’ll 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 effective data science work actually looks like.

To be considered, apply directly through:

https://alpheva. ai/experts/

What you'll do

Depending on the project, you may:

• Analyze: Work with datasets, statistical problems, experiments, and real-world analytical scenarios.
• Evaluate: Review AI-generated analyses, statistical reasoning, models, code, and conclusions.
• Validate: Determine whether AI-generated outputs are statistically sound, technically correct, and supported by the available data.
• Review: Assess data pipelines, analytical approaches, feature engineering, model selection, and evaluation methodologies.
• Reason: Solve realistic data science problems and explain how an experienced data scientist would approach them.
• Compare: Evaluate multiple AI-generated solutions and identify meaningful differences in accuracy, methodology, reasoning, and practical usefulness.
• Create evaluations: Develop challenging data science tasks and criteria for measuring AI performance.
• Provide expert judgment: Identify errors, misleading conclusions, flawed assumptions, data issues, and inappropriate methodologies.

Projects may involve statistical analysis, machine learning, predictive modeling, experimentation, data visualization, forecasting, feature engineering, model evaluation, SQL, Python, and other areas depending on your expertise.

Who we're looking for

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

You may have experience as a:

• Data Scientist
• Senior Data Scientist
• Machine Learning Scientist
• Data Analyst
• Quantitative Analyst
• Decision Scientist
• Statistician
• Research Scientist
• Analytics Consultant
• Machine Learning Engineer
• Data Science Researcher
• Other specialized data professional

You should have:

• Professional experience in data science, statistics, analytics, or a related field
• Strong analytical and quantitative reasoning
• Strong understanding of statistics and data analysis
• Experience working with real-world datasets
• Ability to evaluate analytical methodologies critically
• Strong attention to detail
• Ability to identify errors, biases, and flawed assumptions
• Ability to clearly explain your reasoning
• Strong written communication skills
• Practical understanding of real-world data science workflows

You do not need prior experience working in AI.

What matters most is that you have genuine data science expertise and can apply sound analytical judgment.

What you might work on

You might be asked to:

• Review an AI-generated data analysis and identify errors.
• Evaluate whether a statistical approach is appropriate for a given problem.
• Assess an AI-generated machine learning solution.
• Review model evaluation methodology and identify weaknesses.
• Compare two AI-generated analytical solutions and determine which is stronger.
• Identify data quality issues, statistical errors, or misleading conclusions.
• Evaluate feature engineering or modeling decisions.
• Analyze a realistic dataset and explain your conclusions.
• Review AI-generated Python, SQL, or data science code.
• Create challenging data science problems that can be used to evaluate AI systems.

The goal is not simply to determine whether an answer looks statistically plausible.

The goal is to capture the judgment experienced data scientists use to determine whether an analysis is accurate, rigorous, reproducible, and useful in the real world.

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

Why your expertise matters

AI can generate a data analysis.

But generating a plausible analysis is not the same as demonstrating professional data science judgment.

AI needs to learn how experienced data scientists:

• Understand and validate datasets
• Choose appropriate statistical methods
• Design experiments
• Evaluate models
• Interpret results
• Identify bias and uncertainty
• Recognize data quality issues
• Avoid misleading conclusions
• Make appropriate modeling tradeoffs
• Determine whether an analytical result is actually reliable

That's where you come in.

Your data science expertise helps shape how AI learns to reason about data and solve analytical problems.

How it works

1. Apply

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

Apply directly through the Alpheva Expert Network:

https://alpheva. ai/experts/

2. Get qualified

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

3. Get matched

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

4. Do the work

Complete projects remotely and asynchronously.

5. Get paid

Receive the agreed rate for your completed work.

Help build better AI with your data science expertise.

Apply through the Alpheva Expert Network:

https://alpheva. ai/experts/

Pay: $30. 00 - $300. 00 per hour

Benefits:

• Employee assistance program
• Flexible schedule

Work Location: Remote