Data Scientist; Masters

Data Scientist (Masters) — AI Data Trainer

What if your expertise in machine learning, statistical inference, and data engineering could directly shape how the world's most advanced AI models think and reason? We're looking for data scientists with graduate-level training to challenge, audit, and refine cutting-edge AI systems — exposing their blind spots and helping build models that are sharper, more rigorous, and genuinely trustworthy.

This is a fully remote, flexible contract role. No prior AI industry experience required — just deep domain knowledge and a passion for getting things right.

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

• Design Complex Challenges:
  Craft advanced data science problems spanning hyperparameter optimization, Bayesian inference, cross-validation strategies, dimensionality reduction, and more — pushing AI models to their limits
• Author Ground-Truth Solutions:
  Develop rigorous, step‑by‑step technical solutions including Python/R scripts, SQL queries, and mathematical derivations that serve as the benchmark for AI responses
• Audit AI-Generated Code:
  Evaluate AI outputs using libraries like Scikit-Learn, PyTorch, and Tensor Flow — assessing technical accuracy, efficiency, and best practices
• Refine AI Reasoning:
  Identify and document logical failures such as data leakage, overfitting, and improper handling of imbalanced datasets, then provide structured feedback that improves how models reason through problems

• Currently pursuing or holding a Master's or PhD in Data Science, Statistics, Computer Science, or a quantitative field with a strong data analysis focus
• Solid foundational knowledge across 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
• Naturally meticulous — you catch errors in code syntax, mathematical notation, and statistical conclusions that others miss
• No prior AI or data annotation experience required

• Experience with data annotation, data quality pipelines, or evaluation systems
• Familiarity with production-level data science workflows — MLOps, CI/CD for models, model monitoring
• Hands‑on experience with experiment tracking tools (e.g., MLflow, Weights & Biases)

• Work directly with industry-leading AI research labs on frontier models
• Fully remote and asynchronous — work when and where it suits you
• Flexible contractor arrangement with high autonomy and global reach
• Apply your graduate-level expertise to work that has real, measurable impact on AI development
• Potential for ongoing contracts and expanded project access as new work launches