Data Science AI Trainer

Data Science AI Trainer

Remote | Contract / Project-Based | $40–$125 per hourParo is seeking experienced Data Science professionals for remote, project-based AI training opportunities. This work gives data scientists the opportunity to use their real-world expertise to evaluate and improve how advanced AI models perform complex analytical and technical work. Rather than performing traditional data science work for a single organization, you’ll create realistic professional tasks, evaluate AI-generated analyses and code, identify errors, and determine whether outputs meet the standards you would expect from an experienced practitioner. What You’ll Do Develop challenging data science tasks based on real-world professional workflows. Build or anonymize supporting datasets, schemas, notebooks, metric definitions, and stakeholder materials. Evaluate AI-generated analyses, code, models, SQL queries, forecasts, and written conclusions. Compare AI-generated solutions and document where each succeeds or falls short. Develop detailed grading criteria and tests for technically correct deliverables. Identify issues such as data leakage, confounding, incorrect joins, inappropriate statistical tests, faulty validation approaches, fabricated data, or incorrect interpretation. Contribute across analytics, machine learning, experimentation, forecasting, and data engineering-adjacent work. What We’re Looking For 2+ years of professional applied data science experience preferred. Bachelor’s degree or higher in a quantitative field completed or in progress.

Working proficiency in Python and/or SQL.Ability to write, debug, review, and clearly explain your own analytical code.

Experience in one or more of the followingProduct or business analytics

Business intelligenceApplied machine learning

Statistical modelingExperimentation and A/B testing

Causal inferenceForecasting

Data engineering-adjacent analyticsApplied NLP or computer vision Experience independently managing multi-step analyses from raw or messy data through cleaning, modeling, validation, interpretation, and final conclusions. Strong understanding of concepts such as leakage, confounding, selection effects, statistical power, multiple testing, and model validation.

Strong written communication and analytical reasoning.Current or recent hands-on data science experience strongly preferred.

Familiarity with AI/LLM tools such as ChatGPT or Claude.Ability to independently review complex work and identify subtle errors or weaknesses. Engagement Details