Data Scientist (Masters)

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 systems think and reason?

We're looking for experienced data scientists to challenge, evaluate, and improve cutting-edge AI models — stress-testing their reasoning across complex domains, authoring gold-standard solutions, and helping harden the next generation of AI against the kinds of subtle, technical errors that matter most.

This is a fully remote, flexible contract role built for data science professionals who want meaningful, intellectually stimulating work alongside their existing commitments.

• Organization: Alignerr
• Type: Hourly Contract
• Location: Remote
• Commitment: 10–40 hours/week

• Design Complex Challenges — Create advanced, domain-specific data science problems spanning hyperparameter optimization, Bayesian inference, cross-validation strategies, dimensionality reduction, and more
• Author Ground-Truth Solutions — Build rigorous, 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 AI outputs using libraries like Scikit-Learn, PyTorch, and TensorFlow, assessing them for correctness, efficiency, and technical soundness
• Identify Reasoning Failures — Catch subtle but critical errors in AI reasoning — data leakage, overfitting, improper handling of class imbalance — and provide structured feedback that improves how models think
• Work Independently — Complete task-based assignments on your own schedule, fully asynchronously

• Pursuing or completed a Master's or PhD in Data Science, Statistics, Computer Science, or a quantitative field with a strong emphasis on data analysis
• Strong foundational knowledge across core areas such as supervised/unsupervised learning, deep learning, big data technologies (Spark/Hadoop), or NLP
• Able to communicate highly technical algorithmic concepts and statistical results clearly and concisely in writing
• Naturally detail-oriented — precise when reviewing code syntax, mathematical notation, and the validity of statistical conclusions
• No prior AI or annotation experience required

• Experience with data annotation, data quality assurance, or evaluation systems
• Proficiency in production-level data science workflows — MLOps, CI/CD for models, experiment tracking
• Familiarity with model interpretability, fairness frameworks, or causal inference

• Work directly with industry-leading AI models and top-tier research teams
• Fully remote and flexible — structure your hours around your life
• Freelance autonomy with consistent, intellectually engaging work
• Contribute to AI development that shapes how the world's most powerful models reason about data
• Potential for ongoing contract renewals as new projects launch