Data Scientist (Masters)

Data Scientist (Masters) — AI Data Trainer
About The Role
What if your expertise in machine learning, statistical inference, and data engineering could directly shape how the world's most advanced AI models think and reason? We're looking for Masters-level data scientists to challenge, stress-test, and refine cutting-edge AI systems — helping ensure they reason correctly, write clean code, and handle complex problems with precision.
This is a fully remote, flexible contract role. No prior AI industry experience needed — just deep domain knowledge, sharp analytical instincts, and the ability to communicate technical concepts clearly.
Organization: Alignerr
Type: Hourly Contract
Location: Remote
Commitment: 10–40 hours/week
What You'll Do
Design complex data science challenges across domains like hyperparameter optimization, Bayesian inference, cross-validation strategies, and dimensionality reduction — pushing AI models to their limits
Author rigorous ground-truth solutions including Python/R scripts, SQL queries, and mathematical derivations that serve as the gold standard for model evaluation
Audit AI-generated code and outputs using libraries like Scikit-Learn, PyTorch, and TensorFlow — assessing technical accuracy, efficiency, and correctness
Identify and document reasoning failures such as data leakage, overfitting, and improper handling of imbalanced datasets, then provide structured feedback to sharpen model reasoning
Work independently and asynchronously — fully on your own schedule
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
Deeply knowledgeable in core areas such as supervised and unsupervised learning, deep learning, big data technologies (Spark/Hadoop), or NLP
Able to write clearly and precisely about complex algorithmic concepts and statistical results for technical audiences
Naturally detail-oriented — you catch errors in code syntax, mathematical notation, and statistical reasoning that others miss
Self-motivated and consistent when working independently
No prior AI or data annotation experience required
Nice to Have
Experience with data annotation, data quality evaluation, or AI evaluation systems
Proficiency in production-level data science workflows such as MLOps or CI/CD for models
Familiarity with model benchmarking or technical content authoring
Background spanning multiple data science subfields — the broader your expertise, the more impactful your contributions
Why Join Us
Work directly with industry-leading AI research labs on cutting-edge model development
Fully remote and flexible — work when and where it suits you
Freelance autonomy with the structure of meaningful, task-based work
Make a direct, tangible impact on how advanced AI models reason about data science
Potential for ongoing work and contract extension as new projects launch

Work arrangement
Yes