Data Scientist; Masters

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 data scientists with graduate-level training to challenge, evaluate, and improve cutting‑edge AI models — exposing their blind spots and helping them reason more rigorously about complex technical problems.

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 sophisticated 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:
  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 — assessing correctness, efficiency, and technical soundness
• Refine Model Reasoning:
  Identify flaws in AI reasoning such as data leakage, overfitting, and improper handling of imbalanced datasets, then provide structured, actionable feedback to improve how models think
• Document Failure Modes:
  Systematically record edge cases and reasoning errors to help harden model performance across real-world data science scenarios

• Pursuing or holding a Master's or PhD in Data Science, Statistics, Computer Science, or a quantitative field with strong emphasis on data analysis
• Solid foundational knowledge across supervised and unsupervised learning, deep learning, big data technologies (Spark/Hadoop), or NLP
• Able to communicate complex algorithmic concepts and statistical results clearly in written form
• Naturally precise — you catch errors in code syntax, mathematical notation, and statistical conclusions
• Self‑directed and comfortable working independently on technical tasks
• No prior AI or annotation experience required

• Experience with data annotation, data quality assurance, or evaluation systems
• Familiarity with production‑level data science workflows — MLOps, CI/CD pipelines for models, or model monitoring
• Background in technical writing or academic research communication

• Work directly with industry‑leading AI models and top‑tier research labs
• Fully remote and flexible — work when and where it suits you
• Freelance autonomy with meaningful, intellectually stimulating work
• High agency over your schedule with 10–40 hours per week based on your availability
• Potential for ongoing contract renewals as new AI projects launch