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 systems reason and solve problems? We're looking for data scientists with graduate-level training to challenge, audit, and refine cutting-edge AI models — exposing their blind spots and pushing them to think more rigorously.

This is a fully remote, flexible contract role. No prior AI industry experience required — just deep, proven expertise in data science and a sharp analytical mind.

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

• Design Advanced Challenges:
  Craft complex, domain-specific data science problems spanning hyperparameter optimization, Bayesian inference, cross-validation strategies, dimensionality reduction, and more — specifically designed to stress-test AI reasoning
• Author Ground-Truth Solutions:
  Develop rigorous, step-by-step technical solutions — including Python/R scripts, SQL queries, and mathematical derivations — that serve as definitive reference answers
• Audit AI-Generated Work:
  Critically evaluate AI-produced code (Scikit-Learn, PyTorch, Tensor Flow), data visualizations, and statistical summaries for correctness, efficiency, and best practices
• Sharpen AI Reasoning:
  Identify and document logical failures in AI outputs — data leakage, overfitting, improper handling of imbalanced datasets — and provide structured feedback that directly improves model behavior

• Pursuing or holding a Master's or PhD in Data Science, Statistics, Computer Science, or a quantitative field with strong emphasis on data analysis
• Deeply knowledgeable in core data science domains: supervised/unsupervised learning, deep learning, big data technologies (Spark/Hadoop), or NLP
• Able to communicate complex algorithmic and statistical concepts clearly and precisely in writing
• Highly detail-oriented — you catch errors in code syntax, mathematical notation, and statistical conclusions that others miss
• Self-directed and comfortable working independently on technical tasks
• No prior AI training or annotation experience required

• Experience with data annotation, data quality assurance, or model evaluation workflows
• Proficiency in production-level data science practices — MLOps, CI/CD for models, or similar
• Familiarity with prompt engineering or working directly with large language models

• Work directly with industry-leading AI research teams and large language models at the frontier of AI development
• Fully remote and flexible — work when and where it suits you, on your own schedule
• Freelance autonomy with meaningful, intellectually stimulating technical work
• Make a tangible impact on how AI understands and applies data science at scale
• Potential for ongoing contracts and renewals as new projects launch