Data Science, Quantitative AI Evaluation Specialist

Gramian Consulting

. Evaluate AI-generated outputs across data science, statistics, machine learning, experimentation, and quantitative problem-solving.

Posted 10/14/2026contractRemote • United States, Germany, France, Poland, United KingdomJuniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in evaluating AI-generated outputs through data science, statistics, and machine learning, while providing structured feedback to enhance model capabilities. Possesses strong analytical skills and the ability to communicate complex technical concepts clearly.

Highest-signal resume keywords

Data ScienceMachine LearningStatistical AnalysisAnalytical SkillsTechnical Communication

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Quantitative AnalysisAI EvaluationStatistical Inaccuracy IdentificationPrompt CreationDataset Development

Soft Skills

Critical ThinkingProblem SolvingClear Communication

Industry Keywords

TechnologyFinanceResearchEvidence-Based Feedback

About the role

Key responsibilities & impact

Evaluate AI-generated outputs across data science, statistics, machine learning, experimentation, and quantitative problem-solving.
Create expert-level prompts, datasets, and reference solutions for AI training and evaluation.
Identify flawed methodologies, statistical inaccuracies, unsupported assumptions, and weaknesses in quantitative reasoning.
Assess the correctness, reliability, and clarity of analytical approaches and conclusions.
Provide structured, evidence-based feedback to help improve AI model capabilities.
Apply consistent evaluation criteria and communicate technical findings clearly.

Requirements

What you’ll need

At least one year of professional experience at a recognized company in technology, finance, or research, with some of that experience gained within the past three years.
Foundational knowledge of data science, statistics, machine learning, or quantitative analysis.
Strong analytical and critical-thinking skills, with the ability to identify errors in data-driven reasoning.
Excellent written English and the ability to explain technical and statistical concepts clearly.
Ability to evaluate complex problems systematically and provide accurate, well-supported feedback.
Current residence in an English-speaking country.