Data Science Expert - AI Content Specialist

About the role

What if your deep knowledge of machine learning, statistics, and data engineering could directly influence how the world's most advanced AI systems reason and respond? We're looking for Data Science Experts to join Alignerr's network of specialists helping train and evaluate frontier AI models. This is a fully remote, flexible contract role designed for experienced data scientists who want meaningful, intellectually stimulating work — on their own schedule.

Organization

Type: Hourly Contract

Location

Remote

Commitment

10–40 hours/week

What You'll Do

• Design Complex Challenges: Craft advanced data science problems spanning hyperparameter optimization, Bayesian inference, cross-validation strategies, dimensionality reduction, and more — problems that genuinely test the limits of AI reasoning.
• Author Ground-Truth Solutions: Develop rigorous, step-by-step technical solutions — including Python/R scripts, SQL queries, and mathematical derivations — that serve as the gold standard for model evaluation.
• Audit AI-Generated Code: Evaluate outputs from models using libraries like Scikit-Learn, PyTorch, and TensorFlow for technical correctness, efficiency, and best practices.
• Sharpen AI Reasoning: Identify flawed logic in AI responses — data leakage, overfitting, mishandled imbalanced datasets — and provide structured feedback that directly improves how models think.

Who You Are

• Advanced Degree: Master's (pursuing or completed) or PhD in Data Science, Statistics, Computer Science, or a quantitative discipline with a strong emphasis on data analysis.
• Domain Expertise: Solid foundational knowledge across supervised/unsupervised learning, deep learning, big data technologies (Spark, Hadoop), or NLP.
• Analytical Communicator: Able to explain complex algorithmic and statistical concepts clearly and concisely in writing.
• Precision-Oriented: High attention to detail when reviewing code syntax, mathematical notation, and statistical conclusions.
• No prior AI or annotation experience required — your data science expertise is what matters.
• Nice to Have: Experience with data annotation, data quality review, or AI evaluation workflows; Familiarity with production-level data science practices such as MLOps or CI/CD for model deployment.