Data Scientist (Masters)

About The Role

What if your hard-earned 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 help stress-test and improve cutting-edge AI models — designing challenges that expose their limits, authoring rigorous solutions that set the standard, and auditing AI-generated code for correctness and efficiency.

This is a fully remote, flexible contract role. No prior AI industry experience needed — just deep, demonstrable 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-spanning data science problems across areas like hyperparameter optimization, Bayesian inference, cross-validation strategies, and dimensionality reduction
• 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 Code — Evaluate outputs from libraries like Scikit-Learn, PyTorch, and TensorFlow for technical accuracy, efficiency, and correctness
• Sharpen AI Reasoning — Identify logical failures in AI reasoning — data leakage, overfitting, improper handling of imbalanced datasets — and provide structured feedback that meaningfully improves model performance

• Currently pursuing or holding a Master's or PhD in Data Science, Statistics, Computer Science, or a quantitative field with a heavy emphasis on data analysis
• Strong foundational knowledge across core areas such as supervised/unsupervised learning, deep learning, big data technologies (Spark/Hadoop), or NLP
• Able to communicate complex algorithmic concepts and statistical results clearly and precisely in writing
• Highly detail-oriented — you catch errors in code syntax, mathematical notation, and statistical conclusions before anyone else does
• No prior AI or data annotation experience required

• Prior experience with data annotation, data quality assurance, or AI evaluation systems
• Familiarity with production-level data science workflows — MLOps, CI/CD for models, model monitoring
• Exposure to LLM evaluation or prompt engineering workflows

• Work directly with industry-leading AI research labs on cutting-edge model development
• Fully remote and async — work on your own schedule, from anywhere in the world
• High contractor autonomy with meaningful, intellectually stimulating work
• Direct engagement with state-of-the-art large language models
• Potential for ongoing contract renewals as new projects launch