Data Scientist (Masters)

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 reasons
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

Work arrangement
No