Data Scientist (Masters) — AI Data Trainer About The Role What if your expertise in machine learning, statistics, and data engineering could directly shape how the world's most advanced AI systems think and reason? We're looking for skilled data scientists to challenge, evaluate, and improve cutting-edge AI models — exposing their blind spots, correcting their reasoning, and building the gold-standard solutions they learn from. This is a fully remote, flexible contract role. No prior AI industry experience required — just deep domain knowledge and a sharp analytical mind.

Organization: Alignerr Type: Hourly Contract Location: Remote Commitment: 10–40 hours/week

What You'll Do

Design Advanced Challenges: Craft complex, domain-specific data science problems spanning hyperparameter optimization, Bayesian inference, cross-validation strategies, dimensionality reduction, and more — problems that genuinely stress-test AI reasoning Author Ground-Truth Solutions: Build rigorous, step-by-step reference solutions in Python, R, or SQL — including mathematical derivations, clean code, and annotated reasoning that serve as the definitive benchmark Audit AI-Generated Code: Critically evaluate AI outputs using libraries like Scikit-Learn, PyTorch, and TensorFlow — assessing technical accuracy, efficiency, and correctness of statistical conclusions Sharpen AI Reasoning: Identify and document logical failures such as data leakage, overfitting, improper handling of imbalanced datasets, or flawed statistical inference — then provide structured feedback that directly improves how the model thinks

Who You Are

Pursuing or holding a Master’s or PhD in Data Science, Statistics, Computer Science, or a quantitative field with a strong emphasis on data analysis Solid foundational knowledge across core areas — supervised and unsupervised learning, deep learning, NLP, big data technologies (Spark, Hadoop), or statistical inference Able to communicate complex algorithmic and statistical concepts clearly and precisely in writing Naturally detail-oriented — you catch errors in code syntax, mathematical notation, and statistical reasoning that others overlook No prior AI training or annotation experience required

Nice to Have

Experience with data annotation, data quality assurance, or evaluation systems Familiarity with production-level data science workflows — MLOps, CI/CD pipelines for models, or model monitoring Background in academic research, technical writing, or peer review

Why Join Us

Work directly alongside industry-leading AI research labs on genuinely frontier models Fully remote and async — work when and where it suits you, on your own schedule Freelance autonomy with the consistency of ongoing, task-based project work Make a tangible impact on how the next generation of AI understands and applies data science Strong potential for contract renewal and expanded project involvement as new work launches

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