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 systems reason and problem-solve? We're looking for data scientists with graduate-level training to challenge, audit, and refine cutting-edge AI models — exposing their blind spots and building the ground-truth solutions that make them smarter.

This is a fully remote, flexible contract role. No prior AI industry experience needed — just deep, rigorous command of data science and a sharp eye for technical accuracy.

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

• Design Complex Challenges — Develop advanced data science problems spanning hyperparameter optimization, Bayesian inference, cross‑validation strategies, dimensionality reduction, and more
• Author Ground‑Truth Solutions — Write rigorous, step‑by‑step technical solutions including Python/R scripts, SQL queries, and mathematical derivations that serve as definitive reference answers
• Audit AI‑Generated Code — Evaluate AI outputs using libraries like Scikit‑Learn, PyTorch, and Tensor Flow for correctness, efficiency, and technical soundness
• Sharpen AI Reasoning — Identify logical failures in AI reasoning — data leakage, overfitting, improper handling of imbalanced datasets — and deliver structured feedback that directly improves how these models think
• Document Failure Modes — Systematically record how and where advanced language models break down on data science tasks, helping research teams harden model reliability

• Pursuing or holding a Master's or PhD in Data Science, Statistics, Computer Science, or a quantitative discipline with a strong data focus
• Deeply fluent in core data science concepts: supervised and unsupervised learning, deep learning, statistical inference, and big data technologies
• Able to translate complex algorithmic concepts and statistical results into clear, precise written explanations
• Exceptionally detail‑oriented — you catch errors in code syntax, mathematical notation, and statistical conclusions that others miss
• Comfortable working independently and asynchronously without hand‑holding
• No prior AI or data annotation experience required

• Experience with data annotation, data quality evaluation, or AI model assessment workflows
• Proficiency in production data science environments — MLOps, CI/CD for models, model monitoring
• Familiarity with NLP, computer vision, or large‑scale distributed computing (Spark, Hadoop)
• Background in technical writing, research, or academic publishing

• Work directly with industry‑leading AI research teams and cutting‑edge language models
• Fully remote and flexible — structure your hours around your life
• Freelance autonomy with meaningful, intellectually stimulating work
• Contribute to AI development that shapes the future of data science reasoning at scale
• Potential for ongoing contract renewals as new projects launch