Data Scientist (Masters)

Data Scientist (Masters) — AI Data Trainer
About The Role
What if your deep knowledge of machine learning, statistical inference, and data engineering could directly shape how the world's most advanced AI systems reason and problem-solve?
We're looking for data scientists with advanced degrees to work alongside leading AI research labs — designing expert-level challenges, authoring rigorous solutions, and auditing AI-generated code to make models smarter, more accurate, and more reliable.
This is a fully remote, flexible contract role. No prior AI industry experience required — just serious domain expertise and a sharp analytical mind.
Organization: Alignerr
Type: Hourly Contract
Location: Remote
Commitment: 10–40 hours/week
What You'll Do
Design Advanced Challenges — Create complex, domain-spanning data science problems covering hyperparameter optimization, Bayesian inference, cross-validation strategies, dimensionality reduction, and more
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 AI training
Audit AI-Generated Code — Evaluate model outputs using libraries like Scikit-Learn, PyTorch, and TensorFlow for technical accuracy, efficiency, and correctness
Refine AI Reasoning — Identify logical flaws such as data leakage, overfitting, or improper handling of imbalanced datasets and provide structured feedback to sharpen model thinking
Document Failure Modes — Probe advanced language models on topics like neural network architectures and data engineering pipelines, capturing and reporting every reasoning gap
Who You Are
Pursuing or holding a Master's or PhD in Data Science, Statistics, Computer Science, or a quantitative field with a strong data analysis focus
Strong foundational knowledge across supervised/unsupervised learning, deep learning, big data technologies (Spark/Hadoop), or NLP
Able to communicate highly technical algorithmic and statistical concepts clearly and concisely in writing
Exceptionally detail-oriented when reviewing code syntax, mathematical notation, and the validity of statistical conclusions
Self-directed and comfortable working independently on an async schedule
No prior AI or data annotation experience required
Nice to Have
Experience with data annotation, data quality assurance, or AI evaluation systems
Proficiency in production-level data science workflows — MLOps, CI/CD for models, or similar
Familiarity with model evaluation frameworks or benchmarking methodologies
Why Join Us
Work directly on cutting-edge AI projects alongside world-leading research labs
Fully remote and async — work when and where it suits you
Freelance autonomy with meaningful, intellectually stimulating work
Direct, hands-on engagement with industry-leading large language models
Potential for ongoing contract renewals as new projects launch

Work arrangement
Yes