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 powerful AI systems reason and solve problems? We're looking for advanced data scientists to challenge, audit, and refine cutting-edge AI models — exposing their blind spots, correcting their reasoning, and helping build AI that genuinely understands the science behind the code.
This is a fully remote, flexible contract role designed for Masters and PhD-level data scientists who want to do meaningful, intellectually stimulating work on their own schedule.
Organization: Alignerr
Type: Hourly Contract
Location: Remote
Commitment: 10–40 hours/week
What You'll Do
Design Advanced Challenges: Create rigorous, domain-specific data science problems spanning hyperparameter optimization, Bayesian inference, cross-validation strategies, dimensionality reduction, and more — problems sophisticated enough to genuinely test the limits of frontier AI models
Author Ground-Truth Solutions: Develop precise, step-by-step technical solutions — including Python/R scripts, SQL queries, and mathematical derivations — that serve as authoritative reference answers
Audit AI-Generated Code: Evaluate model outputs using libraries like Scikit-Learn, PyTorch, and TensorFlow for technical accuracy, computational efficiency, and methodological soundness
Identify Reasoning Failures: Pinpoint and document logical flaws in AI reasoning — data leakage, overfitting, improper handling of imbalanced datasets, or flawed statistical conclusions — and provide structured feedback that directly improves model behavior
Work Independently: Complete task-based assignments asynchronously, entirely on your own schedule
Who You Are
Currently pursuing or holding a Masters or PhD in Data Science, Statistics, Computer Science, or a quantitative discipline with heavy emphasis on data analysis
Deeply grounded in core data science fundamentals: supervised and unsupervised learning, deep learning, statistical inference, and model evaluation
Experienced with Python, R, SQL, and standard ML libraries (Scikit-Learn, PyTorch, TensorFlow, or similar)
Able to communicate complex algorithmic concepts and statistical findings clearly and precisely in writing
Exceptionally detail-oriented — you catch syntax errors, mathematical inconsistencies, and flawed statistical conclusions without being prompted
Self-motivated and reliable when working independently without supervision
No prior AI training or annotation experience required
Nice to Have
Familiarity with big data technologies such as Spark or Hadoop
Experience in NLP or large-scale model evaluation
Background in MLOps, CI/CD pipelines for ML, or production-level data science workflows
Prior experience with data annotation, data quality assurance, or evaluation systems
Why Join Us
Work directly with industry-leading AI research labs on genuinely frontier problems
Fully remote and flexible — work when and where it suits you, with no fixed schedule
Freelance autonomy: high agency, task-based structure, and global reach
Engage deeply with state-of-the-art language and reasoning models in ways most data scientists never get to
Potential for ongoing contract renewals as new AI projects launch

Work arrangement
Yes