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 reason through complex problems? We're looking for data scientists with graduate-level training to challenge, evaluate, and improve cutting-edge AI models — exposing their blind spots, correcting their reasoning, and setting the gold standard for what great data science looks like.

This is a fully remote, flexible contract role. No prior AI industry experience required — just deep domain knowledge and a sharp, analytical mind.

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

• Design Advanced Challenges — Craft complex, domain-rich data science problems spanning 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 definitive benchmark for AI responses
• Audit AI-Generated Code — Critically evaluate AI outputs using libraries like Scikit-Learn, PyTorch, and TensorFlow — assessing for technical accuracy, efficiency, and correctness
• Sharpen AI Reasoning — Identify logical flaws in AI thinking — data leakage, overfitting, improper handling of imbalanced datasets — and deliver structured feedback that directly improves model behavior
• Work Independently — Complete task-based assignments asynchronously, fully on your own schedule

• Currently pursuing or holding a Master's or PhD in Data Science, Statistics, Computer Science, or a related quantitative field
• Strong foundational knowledge across core data science domains — supervised and unsupervised learning, deep learning, big data technologies (Spark, Hadoop), or NLP
• Able to communicate complex algorithmic and statistical concepts clearly and precisely in writing
• Meticulous about detail — code syntax, mathematical notation, and statistical validity all matter to you
• Self-motivated and reliable when working independently
• No prior AI or data annotation experience required

• Experience with data annotation, data quality assurance, or AI evaluation workflows
• Familiarity with production-level data science practices — MLOps, CI/CD for models, or similar
• Exposure to model interpretability, fairness, or robustness evaluation
• Background in academic research or technical writing

• Work directly with industry-leading AI research labs and cutting-edge language models
• Fully remote and flexible — work when and where it suits you
• Freelance autonomy with the structure of meaningful, technically rigorous work
• Make a tangible impact on how AI understands and applies data science at scale
• Potential for ongoing work and contract extension as new projects launch