Data Science Expert - AI Content Specialist

Data Science Expert – AI Content Specialist
About The Role
What if your expertise in machine learning, statistical inference, and data engineering could directly shape the intelligence of tomorrow's most powerful AI systems? We're looking for Data Science Experts to stress-test, evaluate, and improve cutting-edge AI models — working remotely on your own schedule.
This is a high-impact contractor role where your deep technical knowledge becomes the benchmark that AI has to meet. You won't just observe AI — you'll challenge it, expose its weaknesses, and help build something genuinely better.
Organization: Alignerr
Type: Hourly Contract
Location: Remote
Commitment: 10–40 hours/week
What You'll Do
Design Advanced Challenges — Create complex, domain-specific data science problems spanning hyperparameter optimization, Bayesian inference, cross-validation strategies, dimensionality reduction, and more
Author Ground-Truth Solutions — Develop rigorous, step-by-step solutions including Python/R scripts, SQL queries, and mathematical derivations that serve as authoritative benchmarks for AI evaluation
Audit AI-Generated Code — Critically review AI outputs using libraries like Scikit-Learn, PyTorch, and TensorFlow, assessing them for correctness, efficiency, and best practices
Refine AI Reasoning — Identify logical failures such as data leakage, overfitting, or mishandled class imbalance, then provide structured feedback that sharpens how the model thinks and responds
Document Failure Modes — Systematically capture edge cases and reasoning breakdowns to help research teams harden model performance
Who You Are
Holds or is pursuing a Master's or PhD in Data Science, Statistics, Computer Science, or a related quantitative field
Strong foundational knowledge across core areas: supervised/unsupervised learning, deep learning, NLP, or big data technologies (Spark, Hadoop)
Able to communicate complex algorithmic and statistical concepts clearly in writing
Highly precise when reviewing code syntax, mathematical notation, and statistical conclusions
Self-directed and comfortable working independently on technical tasks
No prior AI 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 deployment
Prior work reviewing or benchmarking AI/ML systems
Why Join Us
Work directly with industry-leading large language models and AI research teams
Fully remote and async — structure your work around your life, not the other way around
Freelance autonomy with the substance of meaningful, technically rigorous work
Contribute to AI development that sets the standard for how models reason about data science
Potential for ongoing work and contract extension as new projects launch

Work arrangement
No